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Hagiwara, Shun'ichi
IMPACTS OF A REGIONAL HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM ON SMALL METROPOLITAN COMMUNITIES: A CASE STUDY- THE LANSING METROPOLITAN AREA, MICHIGAN
Michigan State University Ph.D. 1982
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University Microfilms International
IMPACTS OF A REGIONAL HIGH-SPEED INTERCITY
PASSENGER TRAIN SYSTEM ON SMALL
METROPOLITAN COMMUNITIES:
A Case Study— The Lansing
Metropolitan Area,
Michigan
By
Shun'ichi Hagiwara
A DISSERTATION
Submitted to Michigan State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
College of Social Science
1982 ABSTRACT
IMPACTS OF A REGIONAL HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM ON SMALL METROPOLITAN COMMUNITIES: A Case Study— The Lansing Metropolitan Area, Michigan
By
Shun'ichi Hagiwara
To date, the intercity passenger in the United
States has principally relied upon such transportation modes as the automobile and the airplane. However, progressively worsening energy resource conditions have made future dependency on such liquid fuel oriented modes of transportation uncertain. The necessity for the development of an alternative transportation mode for the intercity passenger travel market has become increasingly apparent. One alternative mode, the high-speed, intercity passenger train system (HSIPT), could be the most appropriate answer to such a need.
The research for this dissertation was undertaken to examine the probable impacts which would likely occur if a HSIPT system were to be developed in the Great Lakes
Midwest Region. The specific local area selected for this impact study was the Lansing Metropolitan Area, located in the middle of the lower peninsula of Michigan. Shun'ichi Hagiwara
The methods utilized for this impact study were the models of "Population Potential" and "Population
Energy." For the local level/ the "p-Mediam Problem" was adopted to assure objectivity in the selection of a site for the HSIPT system terminal. The "attraction-accessibility model" was used to analyze the probable impacts which would flow outwardly from the HSIPT system terminal site to the surrounding areas within the Lansing Metropolitan Area.
The results of the application of the "Population
Potential" and "Population Energy" models indicate that the communities of Grand Rapids (MI), Columbus (OH) , Cincinnati
(OH), Kalamazoo (MI), and Indianapolis (IN) were likely to be most influenced by the creation of a HSIPT system.
Other communities likely to experience impacts from the creation of a HSIPT system, though less dramatically than the first five, are: Detroit (MI), Cleveland (OH), Lansing
(MI), and London, Ontario (Canada). The results of the application of the "p-Median Problem" and the "attraction- accessibility model" indicate that the creation of a HSIPT system in a local community will likely result in the metamorphosis of the existing urban area. However, prudent location decisions for the various HSIPT system facilities can minimize negative impacts on farm lands, forests, flood plains, and groundwater. ACKNOWLEDGEMENTS
This dissertation research would not have been possible without the direct assistance of many people and my sincere apologies, as well as my grateful thanks, are due to those whose names are excluded from the necessarily brief list which follows.
Professors on my dissertation committee to whom X owe a particular debt include: Dr. Raleigh Barlowe of
Resource Development, Dr. Roger E. Hamlin of Urban Planning,
Dr. John L. Hazard of Marketing and Transportation
Administration, Dr. Robert I. Wittick of Geography, and
Professor Myles G. Boylan of Urban Planning. They gave the firm criticism and supportive direction which were frequently needed to improve my dissertation research.
My special thanks should go to Professor Boylan, the chairman of my dissertation committee, whose guidance, support, and enthusiasm during the past four years were invaluable. I should also like to thank Dr. Rene Hinojosa of
Urban Planning who enthusiastically and patiently helped me to develop the necessary computer programs for this dissertation.
ii I also wish to extend my special thanks to
Dr. Craig Harris of Sociology whose constructive comments
improved my dissertation greatly.
There are a number of other individuals who have also contributed time and expertise to this dissertation project. I wish to express my sincere thanks to
Dr. Etsuo Yamamura of the University of Hokkaido,
Mr. Akira Iriyama of the Japanese National Railways,
Mr. Bud Thar of the Center for International Transportation
Exchange, Mr. William R. Enslin of the Center for Remote
Sensing, and Mr. Jason Whittier of the Lansing Tri-County
Regional Planning Commission.
Also, very special thanks should be extended to my friend, William G. Marx, whose expertise in English, diligence, guidance, and criticism in editing and proofreading improved this dissertation significantly.
Finally, to my wife Miwako and sons, Gaku and
Tadashi, I wish to express my heartfelt thanks for the encouragement and love they have given me throughout the period of my studies at Michigan State University. TABLE OF CONTENTS
Page Chapter
I. INTRODUCTION ...... 1
Background of the Problem {The Issue of Transportation Gaps) ...... 3 Research Objectives ...... 9 Research Assumptions ...... 10 Limits of the Research Scope and S u b s t a n c e ...... 15 Research Methodology ...... 16
II. NATURE AND CHARACTERISTICS OF HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEMS ...... 25
State-of-the-Art of High-Speed Intercity Passenger Train Systems ...... 25 The Roles of a HSIPT System on the Development Process {Case Study: The Shin Kansen and the Japanese Development Process) ...... 37 The Phased Development of the Shin Kansen and the Japanese Development Process ...... 44 Role of High-Speed/ Intercity Passenger Train Systems on Human Contacts and Transactions ...... 63 Positive and Negative Aspects Inherent in the Development of a HSIPT System . . 8 0
III. PROBABLE IMPACTS OF THE CREATION OF A HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM: THE GREAT LAKES MIDWEST REGION - A CASE S T U D Y ...... 86
Development of the Relationales for the Great Lakes HSIPT System Corridors .. . 86 Concepts of "Population Potential" and "Population Energy" as Indices to Measure the Magnitude of the Probable Impacts of a High-Speed Intercity Passenger Train System ...... 94
iv Page Magnitudes of the Impacts of the Creation of a HSIPT System on the Forty Local Communities Within the Great Lakes Midwest Region ...... 104 Changes of the "Population Potential" of the 40 Local Communities Within the Region Before and After the Creation of a HSIPT S y s t e m ...... 110
IV. PROBABLE IMPACTS ON A LOCAL COMMUNITY DUE TO THE CREATION OF A HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM WITHIN THE GREAT LAKES MIDWEST REGION (CASE STUDY: THE LANDING METROPOLITAN AREA IN MICHIGAN) . . . 123
Probable Impacts on Rural-Urban Structures (Impacts on Land Uses and Land Covers and Population Settlement Patterns) . . 12 3 Concept of Accessibility to Analyze the Relationship between Transportation and Land U s e ...... 133 Attraction-Accessibility Indices to Analyze the Population Settlement Patterns in the Lansing Metropolitan A r e a ...... 140 Accessibility to Shopping Opportunities ...... 14 6 Accessibility to Employment Opportunities ...... 150 Accessibility to Urban Functions .... 153 Concept of Per Capita Accessibility: How Does It W o r k ? ...... 160 V. IMPACT PROBABILITIES FLOWING OUTWARD FROM THE SITE DESIGNATED FOR A LANSING HIGH SPEED INTERCITY PASSENGER TRAIN SYSTEM TERMINAL ...... 172
Development for the Rationale for the Site Selection for a Lansing HSIPT System Terminal ...... 173 Impact Probabilities Flowing Outward from the Designated Lansing HSIPT System Terminal Site to the Rest of the Lansing Metropolitan Area .... 184
v Page
VI. SUMMARY AND CONCLUSION...... 201
Summary of the Research Findings ...... 2 01 Regional Impacts ...... 2 02 Terminal Site Selection Impact ...... 2 04 Local Community Impact ...... 208 Conclusions ...... 210 Suggestions for Further Studies ...... 214
APPENDICES ...... A1
BIBLIOGRAPHY ...... 222 LIST OF TABLES
Page
1. Transition of Socio-Economic Characteristics in Japan (1960-1980) ...... 47 2. Population Changes in Five Regions (1960-1980)...... 52 3. Population Changes in Sixty Cities (1960-1980)...... 54 4. The Fares of the Shin Kansen and Air Modes . . 66 5. 1959-1979 Domestic Share by Seven Different Transportation Modes ...... 71 6. Transition of Demographic Influence Due to the Shin Kansen ...... 9 8 7. Number of Passengers at each Terminal of the Tokaido Shin Kansen (1972) 100 8. Population Potential at Each SMSA Based on the Time—Distance by Automobile at b = 1.0 . . . 112 9. Population Potential at Each SMSA Based on the Time-Distance by the HSIPT System at b = 1 . 0 ...... 113 10. Rates of Change of the Population Potential after the Creation of the HSIPT System . . . 114 11. Population Energy (Energy of Interchange) at each SMSA Based on the Time-Distance by Automobile at b = 0 . 5 ...... 117 12. Population Energy (Energy of Interchange) at each SMSA Based on the Time-Distance by the HSIPT System at b = 0 . 5 ...... 118 13. Rate of Increase of Population Energy (Energy of Interchange) after the Creation of the HSIPT System ...... 119 14. Nodal Accessibility to Shopping Opportunities at Township Base and Statistical Relation ship between Accessibility and Population . . 14 9 15. Nodal Accessibility to Employment at Township Base and Statistical Relationship between Accessibility and Population ...... 154 16. Nodal Accessibility to the Urban Functions at Township Base and Statistical Relationship between Accessibility and Population .... 158 17. Nodal Accessibility to the Urban Functions, Per Capita Accessibility to the Urban Function, and Per Capita Income (Township Base) ...... 162
vii Page 18. Name of 4 8 Township Nodes, Their Cartesian Coordinates and Weights for the "p-Median Problem" ...... 176 19. The 192 Nodal Accessibilities to Ten Major Urban Centers (Urban Functions) in the Lansing Metropolitan Area ...... 189
viii LIST OF FIGURES
Page
1. Transportation G a p s ...... 4 2. The Great Lakes Midwest Region Configuration M a p ...... 8 3. Configuration Map of the Japanese HSIPT System (The Shin Kansen S y s t e m ) ...... 30 4. Ranked Potential IPT Corridors (From Market Potential Analysis) ...... 35 5. Ranked Potential TLV Corridors (From Market Potential Analysis) ...... 36 6. Grouping of 46 Prefectures of Japan based on the Phased Development of Shin Kansen .... 45 7. Center of Gravity of Population in Japan (1960-1980)...... 51 8. Four Old Industrial Zones in J a p a n ...... 61 9. Transition of Wage, Number of Automobile, Price of Automobile, Consumer Price Index, Air Fare, and Rail Fare in J a p a n ...... 69 10. Share of Four Transportation Modes for Five Different R a n g e s ...... 72 11. Four Possible Intra-State High-Speed Rail P l a n ...... 88 12. Ohio Intra-State High-Speed Rail P l a n ...... 89 13. Preliminary National High-Speed Ground Transportation Network (Part) ...... 90 14. Geographical Distribution of SMSAs1 Population in the Great Lakes Midwest Region ...... 91 15. Proposed Great Lakes Midwest Regional HSIPT System 9 2 16. The Planned High-Speed Rail Network and 4 0 Local Communities...... 105 17. Location of Nine Major Urban Centers in the Lansing Metropolitan Area 12 6 18. Location of Major Agricultural Lands in the Lansing Metropolitan Area ...... 127 19. Location of Major Woodlands in the Lansing Metropolitan Area 12 8 20. Location of Major Flood Plains in the Lansing Metropolitan Area 12 9 21. Location of Major Groundwater either Sensitive or Developable in the Lansing Metropolitan A r e a ...... 130 22. Possible Locations of the Lansing HSIPT Terminal and HSIPT System Route ...... 132
ix Page
23. Population Growth During the 1970's in the Lansing Metropolitan Area ...... 141 24. The Federal Land Survey System (The Rectangular System) ...... 144 25. Locations of Shopping Opportunities in the Lansing Metropolitan Area and the Levels of Attractiveness (the Amount of Retail Sales in Thousand D o l l a r s ) ...... 147 26. Locations of Employment in the Lansing Metropolitan Area and the Levels of Attractiveness (the Number of Employment)...... 152 27. Locations of Nine Destination Nodes (Nine Cities with Population More Than 2,500) . . . 156 28. Distribution of 48 Townships' Nodal Accessibility ...... 163 29. Distribution of 48 Township Nodes' Per Capita Accessibility ...... 164 30. Isoaccessive Contour Lines Based on the 192 Nodes' Accessibility to Nine Urban Centers (Urban Functions) ...... 168 31. Urban Area in the Lansing Metropolitan Area . . 17 0 32. Geocode System for 4 8 Townships in the Lansing Metropolitan Area ...... 179 33. Geographic Configuration of Ten Urban C e n t e r s ...... 185 34. Locations of New Lansing HSIPT Terminal Node and the Existing Nine Destination Nodes . . . 186 35. Isoaccessive Contour Lines Based on the 192 Nodes' Accessibility to Ten Urban Centers (Urban Functions) ...... 190 36. Concept of the New Urban Corridor in the Lansing Metropolitan Area ...... 193 37. Probable Land Uses in the Lansing Metropolitan Area due to the Development of the Lansing HSIPT Terminal ...... 194 38. Influences of the Development of the Lansing HSIPT Terminal on Land Uses in the Lansing Metropolitan A r e a ...... 196 39. Profile of the New Greater Lansing Area After the Creation of the HSIPT System Terminal in the Lansing Metropolitan A r e a ...... 198
x CHAPTER I
INTRODUCTION
This study is an examination of the probable impacts on rural-urban structural change in typical local metropolitan areas in the United States resulting from the hypothetical creation of a high-speed, intercity passenger train system. The Great Lakes Midwest Regional
Corridor, which stretches from Milwaukee to Chicago to
Pittsburgh and then to Toronto, Canada was selected for this study's locale. A lesser local metropolitan area where the direct impacts of the new system are examined is the Lansing Tri-County Regional Area, which is generally located in the middle of the lower peninsula of the State of Michigan.
The selection of the Great Lakes Midwest Regional
Coridor for evaluation of the development of a high-speed, intercity passenger train system was made for a number of reasons. A high-speed, intercity passenger train system is only feasible in densely populated areas, and the
Great Lakes Midwest Region is one of those densely populated areas in the United States, along with the Northeast Coast and the West Coast Regions. The Great Lakes Midwest
Region has been troubled by a progressively worsening
1 2 economy. The development of a high-speed, intercity passenger train system within this region could be the catalyst for the revitalization of the economic health of this particular region. There are the plentiful coal reserves in the region which can be converted to electricity which would be the primary energy source for a high-speed, intercity train system. The Great Lakes Midwest Region
(hereinafter, the Region) has a balanced transport system which is composed of highly developed, interstate highway systems, rail and air systems, along with St. Lawrence
Seaway System. The addition of a high-speed, intercity passenger train system (hereinafter, the HSIPT system) to the existing multi-modal transportation system could provide another substantial mode to the regional system filling a currently unmet need. Finally, Michigan State
University, where this research has been undertaken, is located in the middle of this particular region, and the immediate accessibility to the data and information necessary for this research is, one self-evident reason for the selection of the Region for the impact study.
The HSIPT system discussed here is a new passenger rail transportation system which is capable of more than
100 miles per hour average speed. Such a HSIPT system has already been developed extensively in several European countries and in Japan. It is being assumed in this research engagement that a HSIPT system has been selected 3 by appropriate authorities to fill the so-called "trans portation gaps" in the United States Midwest as described below.
Background of the Problem (The Issue of Transportation Gaps)
In 1967, Bouladon published a paper entitled
"Transportation Gaps" in which he stressed an apparent lack of transportation concepts and technology to satisfy certain potential markets.^- Those markets fall within two different areas: one ranges over 0.3 to 3 miles from the point where trips are generated; the other area ranges over 30 to 3 00 miles from the same point. These areas should be covered by transportation modes that can travel at 5 to 15 mph and 100 to 300 mph, respectively (Figure 1).
The optimal transportation modes for the former area
(Area II in Figure 1) are bicycle, bus, and subway trans portation systems, and for the latter (Area IV) are helicopter, a high-speed train, and short take-off and landing aircraft (STOL) systems, respectively. In the
United States, both areas have been primarily covered by the automobile.
Gabriel Bouladon, "Transportation Gaps," Battelle (Geneva), April, 1967 cited from A Review of Short Haul Passenger Transportation by Committee on Transportation, National Academy of Sciences, Wash., D.C., 1976. Also, Gabriel Bouladon, "The Transport Gaps (Science Journal) EKISTICS, Vol. 25, Number 146 (Jan. 1968), pp. 6-10. pedestrian
DISTANCE (miles) 0.3 0.6 1 3 6 10 30 60 100 300 600 1000 3000 6000
TIME (minutes) 6.1 7.5 8.8 12.4 15 17.5 24.5 30 35 49 60 65 96.5 120
SPEED (m.p.h.) 2.9 4.8 6.9 14.7 24 .. 34.2 73 120 172 370 600 670 1865 3000
Figure 1,— Transportation Gaps (colored area above) from the paper, "Transportation Gaps," Gabriel Bouladon, Battelle (Geneva) April 1967. Cited from A Review of Short Haul Passenger Transportation, by Committee on Transportation, National Academy of Sciences, Washington, D.C., 1976. 5
Worsening energy resource conditions, however,
cast a shadow over the liquid fuel-oriented mode of
transportation. Not only energy resource problems, but
also recent national legislation such as the National
Environmental Policy Act and the Clean Air Act have made
the liquid fuel-oriented mode of transportation more
vulnerable than ever. Further, the downsizing of the
automobile has made longer trips more uncomfortable and
unsafe. The necessity for the development of alternative
transportation modes for these markets has significantly
increased. Following are some examples of recent commit
ments by the federal and state governments to the develop ment of concepts and technology for the notable
"Transportation Gaps" described above.
The U.S. Department of Transportation, through
agency subdivisions such as the Urban Mass Transportation
Administration (UMTA) and the Federal Railway Administration
(FRA) , has encouraged research into, and the development of, appropriate transportation modes with grants-in-aid.
Research studies evaluating light-rail transit, personal
rail transit, demand and commuter buses, and subway train
systems are those (among others) being supported for the market ranging from 1.0 to 10 or 15 miles from the point where trips are generated. High-speed rail, short take-off and landing aircraft (STOL), and vertical take-off and 6
landing aircraft (VTOL) are being studied for the 3 0 to 2 300 miles zone.
Recently (1980), the Center for International
Transportation Exchange (CITE), a "National Governors'
Association Center of Excellence," was established as the first in a series to serve the governors of the fifty states and U.S. possessions and their transportation advisors to exchange experiences, ideas, and innovative 3 technology with other nations. Among the first assignments identified for exploration and service to states in an action agenda are: the states' role in high-speed, intercity rail transportation; state potential use of electric vehicles; light rail systems for public transit; trans portation finances and management for facilities such as airports, highways, pipelines, public transit, railways, and waterways, including ports; multi-modal terminals for people and freight as used successfully in other nations.
Furthermore, in 1979 and 1980, legislation was passed in Ohio, Michigan, Pennsylvania, and Illinois
2 See, U.S. DOT, High Speed Ground Transportation Alternative Study, Wash., D.C.: The U.S. Government Printing Office, 1973, and National Research Council, Committee on Transportation, A Review of Short Haul Passenger Transportation, Wash., D.C.: National Academy of Sciences, 1976. 3 Information is obtained from CITE, "Center for International Transportation Exchange (mimeo)," East Lansing: CITE, 1980. 7 establishing the Interstate High Speed Intercity Rail
Passenger Network Compact to provide an organizational form for cooperation in the development of the regional project of the Midwest Corridor Improvement Project.
In July, 1980, the Ohio Rail Transportation
Authority (ORTA), which has already invested over three million dollars in feasibility studies for the development of the "Ohio High Speed Intercity Passenger Project," released a report, entitled Ohio High Speed Intercity Rail
Passenger Program, which concludes that the Ohio High
Speed Intercity Rail System is vital and economically feasible.^
Taking into account various investigations, past and present, by public as well as private institutions, this research endeavor focuses on the Great Lakes Midwest
Region. This Region ranges over parts of Wisconsin,
Illinois, Indiana, Michigan, Ohio, Kentucky, Pennsylvania,
New York, and Ontario, Canada (Figure 2). The total area is approximately 35,00 0 square miles and involves ten of the 4 0 largest Standard Metropolitan Statistical Areas
(SMSA) in the United States, namely Buffalo, Chicago,
Cincinnati, Cleveland, Columbus, Detroit, Indianapolis,
Louisville, Milwaukee, and Pittsburgh. Toronto, which is
^The State of Ohio and Dalton, Dalton, and Newport, ORTA - Ohio High Speed Intercity Rail Passenger Program. Phase II. Cleveland: The State of Ohio and Dalton, Dalton, and Newport, 198 0. # Buffalo
HEW YQRKl
00 land______Chicago [Pennsylvania]
[ILLINOIS IlKDIAKA OHIO • Pittsburgh
*Columbu^ U.S. state or Canadian Province • Indi inapolls f j / | WE ST VIRGINIA | One of the 40 largest k * Cincinnati J SMSAs In the U.S. or CMA In Canada • Louisville
KENTUCKY| \
Figure 2.— The Great Lakes Midwest Region Configuration Map. 9 the second largest Census Metropolitan Area (CMA) in
Canada, is also included in this region. If such a new, high-speed, intercity passenger train system were to be installed, roughly 40 million people including 4 million in Canada could possibly benefit from it.
Research Objectives
The objective of this research undertaking is to identify and evaluate the probable impacts of a HSIPT system if it were to be created in a particular region of the United States (such as the Great Lakes Midwest Region).
More specifically, the objective of this research under taking is to identify the probable impacts on local communities of the creation of a HSIPT system.
This overall objective is divided into three parts: first, the probable impacts of a HSIPT system on the local communities in the Region are examined. Second, the probable impacts of a HSIPT system are examined as a competitor (consumer of land) to non-transportation land uses. The probable land use activities in and around the site for a HSIPT system terminal within the prototype metropolitan region and the amount of land necessary to support such land use activities are also investigated in this part. Finally, the probable impacts of new land use activities on the rest of the regional area are analyzed. 10
Research Assumptions
The research undertaking is based on the following assumptions:
1. The installation of a HSIPT system is hypothesized
for the Great Lakes Midwest Region;
2- A HSIPT system corridor is assumed to be located so as
to join 17 U.S. Standard Statistical Metropolitan
Areas (SMSAs) and 3 Canadian Census Metropolitan Areas
(CMAs) in the Region;
3. The Lansing metropolitan area is assumed to be one
terminal point on the Midwestern corridor system;
4. A HSIPT system is assumed to run on an exclusive and
grade-separated rights-of-way (two tracks) to secure
its safety and speed;
5. A HSIPT system is assumed to be capable of more than
100 mph average speed (which requires a maximum speed
of more than 160 mph);
6. A HSIPT system is assumed to serve only passenger,
not freight, operations; and finally,
7. This research undertaking simply assumes that potential
ridership will be forthcoming, that the project is
economically feasible, and that the project is
politically achievable.
The bases for the above assumptions are explained briefly in the following paragraphs. 11
The first assumption, the installation of a HSIPT
system mode, is based on recommendations by such public
institutions as the U.S. Department of Transportation and
the U.S. National Governors' Association. According to
their studies, a HSIPT system is only feasible in such
densely populated corridors as the Northeast Coast between
Boston and Washington, D.C., the West Coast between
Los Angeles and San Diego, the Florida Strip between
Miami and Tampa, the Sun Belt between Dallas and Houston, and the Great Lakes between Chicago and Pittsburgh.^
For instance, the U.S. Department of Transportation's study designates 17 corridors for the Improved Passenger
Train (IPT) system and 18 corridors for the Tracked
Levitated Vehicles (TLV) system and ranks them. For the
IPT system, such corridors as New York-Washington,
New York-Boston, and Los Angeles-San Diego were ranked as the first, second, and third. On the other hand, such corridors in the Great Lakes Region as Chicago-Milwaukee,
Pittsburgh-Detroit, Chicago-Detroit, and Cleveland-
Cincinnati are ranked as the fifth, sixth, tenth, and twelfth, respectively. For the TLV system, such corridors as New York-Washington, New York-Boston, New York-Buffalo,
5 The U.S. Department of Transportation, High Speed Ground Transportation Alternative Study, Washington, D.C.: U.S. DOT, January 1973. The National Governors' Association, Committee on Transportation, Commerce and Technology, Rail Passenger Project, Final Report, Washington, D.C.: NGA, October 1981. 12 and Los Angeles-San Diego were ranked as the first through the fourth. Such Midwestern corridors as Chicago-
Milwaukee-Madison, Pittsburgh-Detroit-Lansing, Chicago-
Detroit, Cleveland-Cincinnati are ranked as the fifth, g sixth, seventh, and thirteenth, respectively. This research undertaking, however, simply assumes that a HSIPT system were to be created in the Great Lakes Midwest
Region, which is one of the higher ranking areas designated by U.S. DOT's study.
The second assumption, the role of linking U.S.
SMSAs and Canadian CMAs, is essentially based on the studies done by the States of Michigan and Ohio and the 7 U.S. Department of Transportation. Although these two states and the U.S. DOT have independently studied possible
HSIPT system networks, it is relatively easy to find out the ideas common to each of their studies. Namely, the potential HSIPT system corridors in the Great Lakes Midwest
Region will join such big SMSAs as Milwaukee, Chicago,
Detroit, Cleveland, Pittsburgh, Cincinnati, Columbus, and
Indianapolis along with such middle-sized SMSAs as Gary,
Kalamazoo, Grand Rapids, Lansing, Flint, Toledo, Akron,
6The U.S. DOT (1973), pp. (1-8)-(1-15). 7 Michigan Department of State Highways and Trans portation, Michigan Railroad Plan, Annual Update, August 197 8, Lansing: Michigan DOT. State of Ohio, Dalton, Dalton, and Newport (1980). The U.S. DOT (1973). 13
Youngstown, and Dayton. In addition to these seventeen
SMSAs in the United States, this research undertaking
assumes that a HSIPT system in the Region should be
extended to three Canadian CMAs such as Toronto, Windsor,
and London where the pivotal functions of the Canadian
economy exist.
The third assumption, the designation of Lansing
as one terminal point, is based on a similar reason
described above for the second assumption. Namely, this
research undertaking simply assumes that a HSIPT system
goes through the Lansing metropolitan area and tries to
find out the impacts of such a new system on land use
activities in the area concerned.
The fourth assumption, the adoption of an exclusive,
grade-separated right-of-way, is based on the writer's
research concerning one HSIPT system which is described
precisely in Chapter II. The most extensive experimenta tion in the field of HSIPT systems has been done in Japan
since 1964. The Japanese HSIPT system adopted the dedicated right-of-way system and has carried more than
1.5 billion passengers without a single casualty.
The fifth assumption, as assumed average speed of more than 100 mph, is based on several studies which
strongly recommend that a HSIPT system run more than
100 mph. The U.S. National Governors' Association's
study, for instance, recommends an average speed of 14
110 mph and the British Ralways' study also recommends
a high speed so as to be competitive with the automobile Q mode of transportation. The Japanese HSIPT system and
the newly emerged French HSIPT system run with average
speeds of 102 mph and 106 mph, respectively.
The sixth assumption, passenger career only, is
based on two circumstances. The first is the concern for
recent declining energy resources which have had a signif
icantly negative impact on liquid fuel oriented modes of
transportation such as the automobile, bus, and airplane used in intercity passenger travel. The development of alternative transportation modes which are relatively non-dependent on petroleum is essential. The high-speed,
intercity passenger train systems being operated in such countries as Japan, England, France, and Germany run with electricity as a power source. In the United States such a source of power can be generated from substantial coal reserves in this country. Nuclear resources also offer a possible means for electricity generation. The second is the rationale for a high-speed, intercity freight train system is basically weak because (except for high valued goods or perishable goods) most goods carried between
g The U.S. National Governors' Association, Committee on Transportation, Commerce and Technology (1981). Klaus Becker, "British Rail's Advanced Passenger Train Enters Service," Advanced Transit News, Vol. 4, Number 3 & 4, pp. 6, 7, 15. 15 cities do not require extremely high speed. If high speed becomes necessary for freight, an air mode could be utilized, regardless of freight or passenger operation.
This second reason is, however, likely to draw considerable argument and the details of that argument will be discussed in Chapter II.
The seventh assumption, forthcoming potential ridership, is axiomatic and needs no further explanation.
Limits of the Research Scope and Substance
The magnitude of an analysis on this subject could be enormous. It has, therefore, been necessary to establish limits to the scope and substance of the study, to establish, in effect, what the study does not include.
As described in the statement of objectives of the research, this undertaking is limited to finding out the impact of a HSIPT system on land use activities based on the seven assumptions mentioned above. The following three components of a complete, comprehensive research analysis are out of the scope of this research undertaking: namely,
1) potential ridership analysis, 2) cost-benefit analysis, and 3) implementation strategies. Needless to say, these three aspects are the keys to the success of a HSIPT system project, but they are substantially beyond the time capabilities and logistics available for this study. 16
Research Methodology
This research undertaking utilizes four models to analyze the probable impacts of a HSIPT system if it were created in the Great Lakes Midwest Region in the United
States. These four models are: 1) and 2) the population potential and population energy models to analyze general impacts of the creation of a HSIPT system in the Region;
3) the attraction-accessibility model to analyze the specific impact of a HSIPT system on land uses; and 4) the location-allocation model, the "p-Median Problem" algorithm so as to make the selection of the site for a local HSIPT system terminal more objective. The first and second models, the population potential and population energy models which were originally developed by J. Q. Stewart in the 1940's, are utilized to examine the probable impacts of a HSIPT system on a total of forty local communities, mostly SMSAs in the United States and a few CMAs in Canada, within the Region.
The first model, population potential, was orig inated from Lagrange's concept of the gravitational potential. In 1773, Lagrange found that where the attrac tion of several planets at once was under consideration , a new mathematical coefficient, not used by Newton, simplified the calculations. This coefficient amounted to a measure of the gravitational influence of a planet of mass m at a distance d, and it was as simple as 17
Q possible, merely m/d. Stewart substituted the population of each city (n) for a planet of mass (m) and called it the "population potential" of the city concerned. This measure could be plotted on a map in the form of
"isopotential contours" which provide a visual presentation of the intensity of human activity, or urbanization, over the map of a region or nation. This measure of population potential has been found to be correlated with demographic and socio-economic patterns of human settlement such as population density, land rents, and newspaper sales from a city to its surrounding areas.^ This concept was later expanded to the concept of the attraction-accessibility interaction models and utilized in analyzing the relation ship between activity locations and the travel behavior of the users of these activities.
The second model, population energy, is based on
Newtonian physics and is an extension of Stewart's concept of "energy of interchange" in which Stewart stressed that
"demographic energy" or "interchange" between a population
and a second population N2 at distance d is times
g J. Q. Stewart, "Empirical Mathematical Rules Concerning the Distribution and Equilibrium of Population," The Geographic Review, Vol. 37, 1948, p. 471.
■^Donald A. Krueckeburg and Arthur L. Silvers, Urban Planning Analysis: Methods and Models, New York: John Wiley & Sons, Inc., 1974, p. 291. J. Q. Stewart, Coasts, Waves, and Weather, Boston: Ginn and Company, 1945, pp. 153-167. 18
Many empirical studies implemented later, however, suggested that the measurement of distance separating the various areas should be raised to some power, for instance, 2 d , as in the case of the Newtonian physics. Using this
"inverse square of distance" concept, it was possible to predict human interactions between pairs of cities such as the number of people traveling by bus, train, or airplane, the number of telephone calls, the volume in tons of 12 railway express shipments, etc.
In the early 1970's, two groups of Japanese scholars adopted the concepts of "population potential" and "population energy" described above and examined the impact of the Tokaido Shin Kansen on the "population potential" of the selected cities along the route and the possible impacts of the planned Tohoku Shin Kansen on the
"population energy" of the selected cities along the 13 planned route. The results of the two groups' studies
1:LJ. Q. Stewart (1948) , p. 473.
^2Kruekeburg and Silvers (1974).
^■^The Tokaido Shin Kansen is the first Japanese HSIPT system which was installed between Tokyo and Osaka which is approximately 350 miles southwest of Tokyo in 1964. The faster train connects these two cities in 3 hours and 10 minutes and the slower train in 4 hours and 14 minutes. The Tohoku Shin Kansen is now being constructed between Tokyo and Sapporo which is approximately 650 miles north of Tokyo. The first stage of the Tohoku Shin Kansen between Tokyo and Morioka which is about 30 0 miles north of Tokyo is expected to be completed in the early part of the year 1982. 19
strongly demonstrated the statistical validity of the 14 above two concepts. Further, their results were compared with the data released by the Japanese National Railways
(JNR) and this writer's own research concerning the impact of the Japanese HSIPT system on regional development.
The consequence of the comparison was very interesting; namely, the result of this writer's research and the data released from the JNR fairly strongly support the results of the researches undertaken by the two groups of Japanese scholars mentioned above. This stimulated and convinced the writer to measure the magnitude of the probable impacts of a HSIPT system on each of the forty local communities within the Region.
The third model, the attraction-accessibility interaction model, is used for two objectives. First, the model is utilized to analyze the relationship between the patterns of human settlement and the locations of various land use activities in a specific local community
(the Lansing metropolitan area). Secondly, the same model is utilized to examine the probable impacts of the creation of a local HSIPT system terminal (a Lansing HSIPT system terminal) on land uses in the community concerned. Specifically, the model is utilized to analyze the impact
1 A Hirozo Ogawa and Etsuo Yamamura, "Kotsu To Toshi Hatten (Transportation and Urban Development),” in Yoshinosuke Yasojima (ed.), Toshi Kotsu Koza (Urban Trans portation Review), Vol. I, 1975, pp. 27-31. 20
probabilities flowing outward from the site designated
for a local HSIPT system terminal to the rest of its local metropolitan area (the Lansing metropolitan area).
The fourth model, the "p-Median" algorithm, is utilized to investigate the optimal location of the site designated for a local HSIPT system terminal. The location of the HSIPT system terminal, as a matter of fact, is a crucial element for the success of the HSIPT system. The
Japanese experience clearly shows that the selection of the site for the HSIPT system terminal has to be done with extreme caution.^ For instance, those sites for a HSIPT system located away from or isolated from the existing urban structures due to political or special interests are often left without the investment dynamics commonly seen in and around the sites for HSIPT system terminals which are developed so as to synchronize with the existing urban structures. Nevertheless, in the public sector, as Rushton mentions, inter-group conflicts often develop over location decisions so that the decision finally made represents a compromise between the wishes of various
Unyu Keizai Kenkyu Senta (Transportation Economy Research Center), Kansen Kosoku Kotsu Taikei no Seibi Hoshiki ni kansuru Kenkyu Chosa Hokokusho (Research Report concerning the Methods for the Possible Improvements of Trunk Express Transportation System), Tokyo: Unyu Keizai Kenkyu Senta, 1980, p. 23. 21
groups, favoring, perhaps, one or another group in 1 g proportion to the political influence each wields.
To alleviate such political and pluralistic
location decision problems, objectivity has to be intro
duced in the decision process. One of the ways to realize
objectivity in the decision process can be the adoption of
location-allocation techniques. The primary objective of
such methods is to locate a necessary public facility for
the maximum effectiveness to users. Among many location-
allocation techniques, the "p-Median problem" is known to
be one of the most important location models. Church and
ReVelle concisely summarize the concept of the "p-Median
problem" as follows:
One of the important measures of the effectiveness of a given locational configuration is the average distance or time that is traveled by those who utilized the facilities. The smaller this quantity, the more accessible the system is to its users. This is appealing, since the smaller the average distance of the travel, the less one is inconvenienced in getting to his closest facility. Therefore, one approach to public facility location planning could be to locate a given number of facilities such that the resulting average travel distance is minimized. . . [The authors explain the equivalence of the concepts of minimizing average travel distance and of minimizing total weighted travel distance, then continue] ..... Within a network context, this location problem can be defined in the following way: Minimize the total weighted travel distance associated with a network of demand nodes by locating p-facilities on the network where each demand node is served by its closest facility. This problem is called the
IS Gerald Rushton, Optimal Location of Facilities, Wentworth: COMPress, Inc., 1979, p. 18. 22
'p-Median problem.1 A set of points that yield the smallest possible weighted distance is called an 'optimal' p-facility solution set/ and the points in the set that minimize the sum of the weighted distances are said to be medians of the network.17
As Rushton mentioned, location decisions in the
public sector are much more difficult to optimize because
of the variety of considerations often deemed to be
relevant in determining a "best location." 18 The intro duction of objectivity as one of key elements in the
location decision process could result in a somewhat more unbiased location decision which would be supportable by various relevant interest groups.
Taking into account the pluralistic location decision process in the public sector described above, the selection of the site for the specific local HSIPT terminal will be approached qualitatively as well as quantitatively. By considering various social, economic, and environmental elements in a specific local area, the
location decision for a HSIPT terminal could be qualita tively made. By adopting the location-allocation technique, the decision could be quantitatively made.
17Richard L. Church and Charles S. ReVelle, "Theoretical and Computational Links between the p-Median, Location Set-covering, and the Maximal Covering Location Problem," Geographical Analysis, Vol. VIII, Oct. 1976, p. 407. 18 Gerale Rushton, p. 17. 23
The quantitative part of the analysis will be done by adopting one of the MSU's software programs, FLOW, developed by Dr. Robert I. Wittick of Michigan State
University. FLOW is a large overlay system designed to analyze and map qualitative or quantitative flow data.
The "p-Median problem" algorithm is one of the twelve modules accommodated in the FLOW program and is approach- 19 able by both batch and interactive systems.
The final product of the attraction-accessibility model is the "isopotential contour" map based on the locations of attractions and human settlements. By producing this "isopotential" map and referring it to the existing land use activities in a specific local metro politan area, it is possible to avoid unnecessary conflicts between the developmental potentiality and necessary land management to preserve productive agricultural land, forest land, water resources, etc. which are irreplaceable natural resources for a living environment of essential quality. In this regard, the results of this consideration of the conservation of life-support bases would be extremely crucial for both public and private policy decision makers.
For public decision makers, the result will be referred to for future action in land management, land use control,
19 Robert I. Wittick, FLOW, Version 5, Technical Report 4 (MSU Software Reference Manual) , Sept. 1980, East Lansing: MSU. 24 public investment/ etc., and for private decision makers, it will help to develop future investment strategies within the area concerned. More importantly, however, the application of this research method will be important to any of the potential sites for a HSIPT system terminal and to the surrounding areas within the region where a
HSIPT system is planned or expected.
This introductory chapter presented the background of the problem concerning the HSIPT system in the United
States; that is, this chapter presented an evaluation of the status of transportation concepts and technologies which are needed to satisfy certain potential markets in the U.S. transportation system. This chapter further stated research objectives, research assumptions, limits of the research scope and substance, and research meth odologies. In the following chapter, the nature and characteristics of high-speed, intercity passenger train systems are presented and examined. In doing so, the forthcoming chapter provides an essential background for understanding the various elements to be considered in designing future HSIPT systems in the United States or anywhere else. CHAPTER II
NATURE AND CHARACTERISTICS OF HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEMS
In this chapter, the nature and characteristics of
high-speed, intercity passenger train systems are presented
and examined. In order, this chapter examines current
HSIPT already in operation in Europe and Japan, current
state of the affairs regarding the development of HSIPT
systems in the United States, the roles of a HSIPT system on the development process (the phased development of the
Shin Kansen and the Japanese development process), the role of high-speed, intercity passenger train systems on human contacts and transactions, and the positive and negative aspects inherent in the development of a HSIPT system. In so doing, this chapter provides an essential background for understanding the various elements to be considered in designing future HSIPT systems in the United
States or anywhere else.
State-of-the-Art of High-Speed Intercity Passenger Train Systems
In September 1981, the French National Railroad
(S.N.C.F.) unveiled the world's fastest train, which is capable of 236 mph. The S.N.C.F. has spent $1.6 billion
25 26 since 197 0 to develop this new train, the TGV, which stands for train "a grande vitesse" (of great speed).
By the end of October, 1981, 38 TGV trains from Paris to
Lyon will be in daily operation. In 1983, TGV track will be extended to Marseille, and the present 4 hours and 20 50 minute ride will be trimmed to 2 hours and 50 minutes.
The TGV's 236 mph capacity outmoded the Japanese Shin
Kansen's capacity of 165 mph. However, the average speed of the TGV between Paris and Lyon was a mere 106 mph, which is slightly faster than the present Japanese Shin
Kansen's average speed of 102 mph. The reason for this low average speed is that the TGV has to run the first
78 miles on the S.N.C.F.'s regular track. The remaining special track built for the TGV has continuous welded rails and concrete "sleepers" for stability. The TGV needs 2 miles to stop at 162 mph; therefore, there are no grade crossing over this exclusively dedicated right-of- way.
In Great Britain, the High Speed Train (HST), currently, the world's fastest diesel train, has been in operation since 1976. There are now 160 HST services every weekday, 100 of these serving routes from London
Paddington to South Wales and Bristol and 60 on the London
King's Cross to Edinburgh line. The average speed of the
2 0 Michael Demarest, "Entrez the Flying Peacock," TIME, October 5, 1981, p. 51. 27
HST system is approximately 93 mph. This high speed on
conventional rail tracks was achieved by the introduction
of a powered tilting body design plus a much lighter 21 vehicle with a low center of gravity.
British Rail (BR)'s ultimate goal is, however, to
replace the HST system with the APT (Advanced Passenger
Train) system in the near future. The APT system,
originally developed by using gas turbines as the driving
force, did reach 245 km/h on August 1975; however, the
gas turbines were never satisfactory and quadrupling oil
prices have made them uneconomical. BR eventually converted
its energy source to electricity, as did the French and
Japanese high-speed rail systems.
The primary reasons for this conversion from the
HST to APT are included in a major study concerning the
future of intercity business carried out in 1971 by BR.
The study confirmed that customers— potential and actual— put journey time at the top of their priorities when choosing their mode of travel. Amenities (quietness, a good ride, air-conditioning) came second, followed by reliability. It was the firm conclusion of this study that if rolling stock was renewed on a like-for-like basis,
21 I. M. Campbell, Intercity Rail Passenger Develop ment in Britain, published for the Annual Conference of the United States National Governors' Association, Denver, August, 19 80. British Railways Board (Central Publicity Unit) S1048. 28 with no improvement in speed in 1970, BR would see its market share decline steadily. Conversely, a substantial improvement in journey times would bring large amounts of extra business, even though fares were pushed up in real terms to reflect the better service offered. Because BR intends to utilize existing rail tracks, the available routes for the APT service are limited to routes from
London to Bristol and South Wales, along with the route from London to Edinburgh where the present HST system is in operation. The remaining routes have serious curves 22 which prevent high-speed operation.
In Japan, the first Shin Kansen was built in 1964 between Tokyo and Osaka, a city approximately 4 00 miles to the southwest of Tokyo. Eight years later, in 1972, the first phase of the Sanyo Shin Kansen was built between
Osaka and Okayama, a city approximately 100 miles to the southwest of Osaka. Further, in 1975, the second phase of the Sanyo Shin Kansen between Okayama and Fukuoka
(Hakata is the name of station in this 1 million population city} was completed. Two more Shin Kansens are now being constructed. The first phase of the Tohoku Shin Kansen
22 Klaus Becker (no date), pp. 6, 7, 15. As noted, the French system runs with an average speed of 106 mph and the Japanese system with 102 mph. To achieve 102 mph, the minimum radius of curves must be designed to be 4,000 meters. Many of the curves on the BR's existing tracks are less than 2,000 meters radius because the cost of curve realignment is prohibitive. 29 between Tokyo and Morioka, a city approximately 300 miles to the north of Tokyo, will start service sometime early in 1982, and its extension to Sapporo (Phase 2) is expected to be finished by the end of 198 4. The other line, the
Joetsu Shin Kansen between Tokyo and Niigata, a city approximately 2 00 miles to the northwest of Tokyo, is also expected in 1982 (Figure 3). Further, the Japanese
National Railways' MLV (Magnetic Levitation Vehicle) has repeatedly attained speeds in the range of 500 to 5 40 km/h
(312 to 337 mph) over a 7 km (4.4 miles) test track.
Making this MLV technology a base, the JNR and the Japanese government have been studying the possibility of a 2nd
Tokaido Shin Kansen between Tokyo and Osaka. If this second Tokaido Shin Kansen were completed, those two cities could be reached within one hour and a half, or less. The field survey for this project started in the late 1970's. The date of the completion is unknown, but presumably it will be finished by the end of the 1990's.
Although it is undeniable that Europe and Japan are clearly ahead in developing improved rail passenger transportation, several U.S. states have already taken some actions to realize a new, high-speed, intercity passenger train system. As early as in 1975, Ohio estab lished the Ohio Rail Transportation Authority (ORTA) and has already spent more than $3 million to study a HSIPT system, depending mainly on the Japanese experience for 30
Sapporo The 5hln kansen in operation
The Shin kansana under construction kodate
chinoe
Sanyo Shin Kansen hima Phase II .fhase I iyama March, 1975 March, noniya 1972 kasak
Yokohama
^tagoya SQB.Z mamatstu
Kiftakyua o Tokaido Shin October, 1964
Figure 3.— Configuration Map of the Japanese HSIPT System (The Shin Kansen System). 31
guidance. In 1979, Michigan's Senate and House approved
a bill to study a high-speed, interstate train system,
and a ridership analysis for an intra-state, high-speed
rail passenger system is now being undertaken by the
Michigan Department of Transportation, utilizing the
expertise of the British consulting firm, TRANSMARK.
As mentioned before, in 1979 and 1980 legislation
was passed in Ohio, Michigan, Pennsylvania, and Illinois
to establish the Interstate High Speed Intercity Rail
Passenger Network Compact to provide an organizational
form for cooperation in the development of the Midwest
Corridor Improvement Project.
Not only such legislative actions but also actual
actions to realize a high-speed, intercity passenger train
system have been taken in several states and by AMTRAK.
These actions are basically classified into two different
approaches. The first approach could be called the British approach, which aims at the service improvement of existing
systems. Existing tracks and signals are the objects of
the improvement effort, and a joint freight-passenger operation is the characteristic method of this approach.
Michigan, for instance, is following this approach. The tentative goal aimed at by Michigan is to provide a 32
2 3 maximum 79 mph service. California and Illinois are examining the possibility of providing more frequent service necessary for true corridor operation through the
4 03(b) program which authorizes AMTRAK to operate selected services at the request of a state or local agency, with 24 the state providing 50% of the operating expenses.
The second approach could be called the Japanese or French approach. This approach provides an exclusive right-of-way to a high-speed, intercity passenger train system. This exclusive right-of-way is secured by a protective fence, an elevated rail line, or an open ditch system; consequently, no grade crossings can be provided. California and Florida are now investigating the possibility of an exclusive 25 right-of-way for high-speed operations, and Ohio has 2 6 already designed detailed plans for such a system.
AMTRAK, with the assistance of the JNR, is now undertaking design and engineering studies for a possible HSIPT system on four selected corridors. This study evaluates routes
23 Michigan Department of State Highway and Trans portation, Michigan Railroad Plan Annual Update August 1978, Lansing: Michigan DOT. 24 NGA Committee on Transportation, Commerce, and Technology, p. 3.
25Ibid., p. 3. 2 6 The State of Ohio and Dalton, Dalton, and Newport (1980). 33 from Los Angeles to San Diego, Dallas-Fort Worth to
Houston, Miami through Orlando to Tampa, and one route 27 from Chicago.
The U.S. Department of Transportation, on the other hand, implemented a study concerning high-speed ground transportation and released a report entitled,
High Speed Ground Transportation Alternative Study, in
January, 1973. This HSGT study concludes that two rail passenger systems could be implemented in the United States.
One is the "Improved Passenger Train (IPT)" system and the other is the "Tracked Levitated Vehicle (TLV)" system.
The IPT system concept is aimed at revitalization of past investments in conventional rdil routes by the introduction of 1) attractive, comfortable and reliable equipment,
2) reliable schedules to give convenient, frequent service,
3) speeds competitive with air modes in the 50 to 200 mile trip range, and 4) minimal adverse environmental impact.
This IPT aims to provide greater comfort and convenience than conventional service at speeds 30 to 5 0% higher on curves and up to 150 mph on the straightaway. The TLV, on the other hand, aims at a speed competitive with air up to 4 00 miles. The intercity TLV system concept features speeds up to 300 mph. The IPT system is similar to the
27 The Lansing State Journal, 10 August 1981 and the NGA Committee on Transportation, Commerce and Technology (1981), p. 3. 34
HSIPT systems which have been implemented in Europe and
Japan, and the TLV system is equivalent to the MLV system which is being developed in Japan and Germany.
As mentioned earlier, the U.S. DOT'S study ranked seventeen corridors for the IPT system and eighteen corridors for the TLV system based on its market potential analyses (Figures 4 and 5), but concluded that system viability and benefits thoroughly depend on future petroleum fuel developments. 2 8
As can be seen, the time and place of eventual implementation for a HSIPT in the United States are uncertain. However, it is important to note that an appropriate system in one country may not be appropriate in others. In fact, each country's system needs to be dictated by its own geographic, demographic, socio-economic, political and even cultural structures. In fact, the introduction of a HSIPT system may lead to a drastic structural change of the U.S. economy. Future petroleum fuel shortages, on the other hand, if they occur, may jolt the U.S. societal system which has been dependent on the mobility offered by the automobile mode of transportation.
Needless to say, the circumstances in the United States require more careful analysis than that by other nations where existing systems are better suited to such a
2 8 The U.S. Department of Transportation (1973), pp. (I— 1)-(1-5). 35
Corridors
1. New York-Washingcon 10. Chicago-Detroic 2. New York-Boston 11. Los Angeles-Lss Vegas 3. Los Angeles-San Diego 12. Claveland-Cincinna c i 4. New York-Bu£falo 13. Los Angela-San Francisco 5. Chicago-Mllwaukee 14. Piccsburgh-Philadelphia 6. Pittsburgh-Detrolt 15. Chicago-St. Louis 7. Washington-Norfolk/Nevport News 16. Tampa-Orlando 8. Springfield/Hartford-New York 17. Seattle-Portland 9. San Franclsco-Sacraisento
Figure 4.— Ranked Potential IPT Corridors (From Market Potential Analysis).
SOURCE: The U.S. DOT, High Speed Ground Transporta tion Alternative Study, January 1973, pp. (1-9). The map shown above is compiled by Shun'ichi Hagiwara. 36
Corridors
1. New York-Washlngtou 10. Springfield/Hartford-New York 2. New York-Boaton -New York 3. New York-Buffalo 11. Los Angeles-Lao Vegas 4. Loa Angeles-San Diego 12. Chicago-SC. Louis 5. Chicago-Milwaukee-Hadiaon 13. Cleveland-Clnclunatl 6. Plttsburgh-Decrolt-Lanalng 14. San Franclsco-SacrsaenCo 7. Chlcago-Datole 15. Durham-Atlanta 8. tfashington-Norfolk/Newport News 16. Jacksonville-Miami 9. Loa Angeles-San Francisco 17. Fhlladelphla-Plccsburgh 18. Seactle-Portland
Figure 5.— Ranked Potential TLV Corridors (From Market Potential Analysis).
SOURCE: The U.S. DOT, High Speed Ground Transporta tion Alternative Study, January 1973, pp. (1-14). The map shown above is compiled by Shun'ichi Hagiwara. 37
transportation system as a HSIPT system. Nevertheless,
many valuable lessons can be learned from other countries
which have already encountered the various problems inherent
in the creation of a HSIPT system. Especially, the long
and successful operation of the HSIPT system in Japan can
provide reliable and substantial data to the United States.
Therefore, a description and analysis of the Japanese Shin
Kansen are provided as one working prototype which could
be applicable if such a system were to be created in the
United States.
The Roles of a HSIPT System on the Development Process (Case Study: The Shin Kansen and the Japanese Development Process)
It is reasonable to conceive that the creation of a HSIPT system in any region or in any country not only reduces the time-distance between the communities joined by the system, but it also stimulates new development in each of those regions or areas served by it. For instance, the aforementioned Ohio Rail Transportation Authority
(ORTA) stresses the benefits of a HSIPT system as follows: 2 9 namely, a HSIPT system would:
1. create engineering and construction jobs during the
design and building phases;
29The ORTA (1980), pp. 49-51. 38
2. create jobs in the operation, maintenance, and control
of the rail network once it was operational;
3. utilize the Region's vast reserves of coal to provide
electricity for the HSIPT system;
4. stimulate the coal industry;
5. stimulate investments in new enterprises around the
terminals;
6. help attract and retain businesses seeking a location
for expansion;
7. utilize high technologies and skills developed and used
in the automobile and auto-parts industries in the
Region for necessary materials, equipment, and
machinery;
8. facilitate efficient and economical movement of the
population, and;
9. stimulate and strengthen economic growth of the local
area of any given terminal, as well as for the region
where the system is planned.
The U.S. Governors' Association's study also mentions the possible benefits of the creation of a HSIPT 30 system in the United States as follows:
The Europeans and Japanese have built an entire industry based on providing rail passenger service and the equipment and technology necessary to operate it. This industry provides thousands of highly
"^The NGA Committee on Transportation, Commerce and Technology (1981), p. 2. 39
productive jobs in the service, technological and industrial sectors of their economies. . . . Even if we were to decide tomorrow to launch a massive high-speed rail passenger improvement program, we would be forced to import foreign technology and equipment at least for the short term. Instead, we should be developing a domestic industry providing badly needed jobs, especially decreased demand for steel and autos. Such innovative industry will continue to grow and provide jobs.
These extremely positive expectations for the role of transportation modes such as a HSIPT system in the economic development process, however, have been the subject of strong criticism. According to such criticism, development is not the single determining process, and the singling out of a single component such as investment in the highway program or rail improvement program as a decisive element is nothing but an oversimplification of 31 a very complex development process. Nevertheless, the positive view concerning the role of transportation in the development process is still remarkably resilient. In
Japan, for instance, a number of studies have stressed a significant role of the Shin Kansen in the development 32 process xn Japan.
31 Howard Gauthier, "Geography, Transportation, and Regional Development," Economic Geography, Vol. 46, 197 0, pp. 612-619. 32 Concerning the studies related to the role of the Shin Kansen, see Shun'ichi Hagiwara, Possible Socio- Economic Effects of the Development of the High-Speed Rail Transit Service in Underdeveloped Areas in Japan, Unpublished Master's Thesis, Michigan State University, East Lansing, Michigan, 1977. 40
In the United States, Goldberg cited Paul Wendt's
study concerning the San Francisco Bay Area and Vancouver,
British Columbia, Canada, and stressed that the areas which grew most rapidly in terms of population and value of land improvements were those areas which were opened up as a result of transportation improvements. According to him, the San Francisco Bay Area has experienced dramatic improvements in transportation and internal accessibility first by the automobile, then by the San Francisco-Oakland
Bay Bridge and the Golden Gate Bridge, and more recently 33 by the freeway system.
Recently, however, a pessimistic view concerning the role of transportation has emerged. This view holds that the creation of transportation may absorb some portion of scarce resources that should be employed elsewhere.
The highway extension program has been halted in many states and cities in the United States. The plan to create the total of 7,200 km (4,500 miles) Shin Kansen network all over Japan is now regarded as a waste of scarce resources and funds. This pessimistic view will emerge more and more strongly in the decision process for the allocation of resources to transportation systems such as the HSIPT system. In fact, errors in the allocation of
33 Michael A. Goldberg, "Transportation, Urban Land Values, and Rents: A Synthesis," Land Economics, 46, 1970, pp. 153-162. 41 resources often happens in the sector of transportation.
Gauthier summarizes the reasons for such a misallocation of resources to the transportation sector as follows: 34
1) the lumpiness, longevity, and externalities associated with transportation capital create greater hazards in calculating and specifying future benefits and costs; and 2) there is a belief that transport is a safe investment politically.
In fact, inappropriately located airports, highway interchanges, and rail terminals are commonly seen every where. Improper and unwise management of scarce resources can be avoidable only by proper planning and careful cost and benefit analysis. Proper decision making is often only obtained from valuable empirical lessons.
In the following text, the role of the Shin Kansen in the Japanese development process will be examined in three different aspects. First is the impact of the system in the regional development process. As mentioned before, it took about ten years to complete the 1,070 km (670 miles) Shin Kansen between Tokyo and Fukuoka, and it is taking about ten years to finish up another 1,040 km
(650 miles) between Tokyo and Sapporo. This part of the research, accordingly, tries to examine whether a strong relationship exists between the phased development of the
Shin Kansen and regional development. Second is the role of the system in the enhancement of human contacts and
34Gauthier (1970), p. 614. 42
transactions. Traditionally, much attention has been
given to the logistics of raw materials and finished products. Recently, however, the significance of the exchange of messages through the transport of people has been recognized as a key element of regional development. 35 Thorngren, for instance, says that:
Technology, market conditions, and social values are changing at a more and more rapid pace. When changes are considered, contacts between various parties are often necessary. The actors are often spread over space. The exchange of information can take the form of face to face contacts, or can use technical devices.
William Alonso also supports the significance of human 3 6 contacts as follows:
Industries may now be attracted to areas of good weather, either because it is important to their locations (as in the case of the aircraft industry) or because it will be attractive to their workers (as in the case of some research industries). Or they may be attracted to special site advantages, or to cheap labor, or perhaps most important, to contacts. These contacts are infinitely varied in their forms. They may be managerial exchanges, where vital infor mation is exchanged casually over lunch, or close supplier-customer coordination, or the chance remark that discloses an unsuspected opportunity, or the shoptalk of technical people that stimulates new ideas. The importance of contacts will probably increase the attraction of large urban centers for many industries, and lead to further concentration.
35 B. Thorngren, "How do Contact Systems Affect Regional Development?" Environment and Planning, Vol. 12, 1970, p. 409. 3 6 William Alonso, "Location Theory," in John Friedmann and William Alonso, eds. , Regional Policy: Readings in Theory and Application, Cambridge: The MIT Press, 1975, pp. 35-63. 43
Thorngren, also, summarizes the propositions based on the
assumptions concerning the necessity of the face-to-face
contacts in the economic development process in the post- 37 industrial society as follows:
1. the volume of contacts between different firms, research bodies, organizations and authorities is great. It is expected to increase considerably; 2. the contact work is mainly performed by people in higher positions. It is expected to involve an increasing proportion of employees, in the lower echelons also; 3. the means of communication now available do not permit a separation of contact-dependent functions and others within organizations. Even new devices, such as TV-phones or data terminals, cannot be expected to change this within the next few decades; 4. instead, new techniques for telecommunication may - by permitting a spatial division of the firm's administrative and production units - increase the tendencies towards growth within the bigger urban regions.
To make people achieve face-to-face contacts, a network system which is faster, cheaper, more accessible, or more efficient than others is necessary. Gunner Toernqvist stresses the necessity of such a network as follows: 3 8
If people have to meet other people long distance away, then they have to use express trains or air services. The resulting contact system can be linked to a strongly focused network, consisting of a set of nodes linked by fixed transport routes, which people must use for getting from one place to another.
■^B. Thorngren (1970) , p. 410. 38 Gunner Toernqvist, "The Geography of Economic Activities: Some Critical Viewpoints on Theory and Application," Economic Geography, Vol. 53, No. 2, April 1977, p. 158. 44
This part of the research, accordingly, examines whether the Shin Kansen has been, and such a HSIPT system as the
Shin Kansen, will be the key channel for human contacts and transactions in any region or in any country. Finally, various problems inherent in the creation of a HSIPT system will be investigated. Some of those are negative and others are positive. Some of these might have been predicted or foreseen, but others may have to be learned only from experience. In this regard, the various problems dealt with in creation of the Shin Kansen could be valuable for HSIPT system planners in any region or any country.
Accordingly, the following thirty pages of this text are devoted to analyzing the Shin Kansen and to comparing similarities and potentials for the United States.
The Phased Development of the Shin Kansen and the Japanese Development Process
In this analysis, forty-six Japanese prefectures are divided into four groups (Figure 6). Group A is composed of nine prefectures located in the northern part of Japan where the service of the Tohoku and Joetsu Shin
Kansens are expected between 1982 and 1984. Group B consists of the fourteen prefectures located along the
Tokaido Shin Kansen route which have had the service of the Shin Kansen since 1964. Group C is composed of four prefectures along the Sanyo Shin Kansen; the service of 45
46 Prefectures excluding Okinawa
1. Hokkaido 2. Aomori 3. Ivate 4. Miyagi 5. Akita Hokkaido 6. Yamagata 7. Fukushima 8. Ibaragl 9. Tochigi 10. Gumma 11. Saltama 12. Chiba 13. Tokyo 14. Kanagava 16. Niigata 36. Tokushima 16. Toyama 37. Kagawa 17. lahikawa 38. Ehime 18. Fukui 39. Kochi 19. Yamanashi 40. Fukuoka 20. Nagano Saga 21. Gifu 42. Nagasaki 22. Shi2uoka 43. Kumamoto 23. Aichi 44. Oita 24. Mie 45. Miyazaki 25. Shiga 46. Kagoshima 26. Kyoto 27. Oeaka 28. Hyogo 29. Nara 30. Wakayama 31. Tottori 32. Shlmane 33. Okayama Honshu 34. Hiroshima 35. Yamaguchi
Shikoku Group A (9 Prefa.) * | Group B(14 Prefa.) Kyuahu Group C(4 Prefa.) J Group D(19 Prefs.)
Figure 6.— Grouping of 46 Prefectures of Japan based on the Phased Development of Shin Kansen. 46
the Shin Kansen was given to these prefectures partly in
1972 and completely in 1975. The rest of the prefectures
(nineteen prefectures) comprise Group D. No service of
the Shin Kansen has been given to this group, and there
will be no service in the near future.
Five socio-economic variables were selected in
order to analyze the effects of the Shin Kansen on regional
development. Those were: 1) prefectural population,
2) urban population (in other words, DID population, densely inhabited district population), 3) the number of manufacturing establishments, 4) the number of service-
oriented establishments, and 5) the value of shipments.
The data for these five variables were looked at for the years of 1960 , 1965 , 1970, and 1975 , respectively (Table 1).
For the year of 198 0, only the prefectural population was
available.
The data shown in Table 1 can be interpreted as
follows:
1. During the first five years of the 1960's (1960-65),
Group B, which is composed of 14 prefectures along
the Tokaido Shin Kansen route, expanded its dominant
status in every category. The share of the prefectural
population increased from 44.9 to 48.6 percent, while
the population in densely inhabited districts increased
from 60.9 to 65.3 percent. The shares in both man
ufacturing and service-related establishments also TABLE 1.— Transition of Socio-economic Characteristics in Japan (1960-1980). 1960 1960/1965 1965/1970 1970/1975 1975/1980
Area Growth Share Growth Share Growth Share Growth Share Growth Share Rate Z Rate X Rate X Rate X Rate Z
National 1.000 100.0 1.052 100.0 1.055 100.0 1.069 100.0 1.046 100.0 Prefectural Group A 1.000 20.6 1.001 19.7 1.009 18.8 1.042 18.3 1.048 18.4 Population Group B 1.000 44.9 1.137 48.6 1.117 51.4 1.100 52.8 1.050 53.1 Group C 1.000 10.1 0.997 9.6 1.026 9.3 1.065 9.3 1.043 9.3 Group D 1.000 24.3 .961 22.2 .975 20.5 1.020 19.6 1.032 19.3
National 1.000 100.0 1.158 100.0 1.165 100.0 1.149 100.0 Population in Group A 1.000 12.7 1.229 13.5 1.151 13.3 1.124 13.1 Densely Group B 1.000 60.9 1.243 65.3 1.203 67.5 1.131 67.7 N.A. Inhabited Group C 1.000 8.7 1.183 8.8 1.112 8.4 1.121 8.2 District (DID) Group D 1.000 17.8 0.802 12.3 1.001 10.8 1.181 11.0
National 1.000 100.0 1.209 100.0 1.104 100.0 1.103 100.0 Manufacturing Group A 1.000 14.0 1.221 14.1 1.074 13.7 1.106 13.8 Establishments Group B 1.000 58.0 1.281 61.5 1.095 61.0 1.124 62.2 N.A. Group C 1.000 8.7 1.0B5 7.8 1.011 7.1 1.023 6.6 Group D 1.000 19.4 1.039 16.6 1.204 18.1 1.058 17.4
National 1.000 100.0 1.157 100.0. 1.095 100.0 1.167 100.0 Services Group A 1.000 18.8 1.157 18.7 1.075 18.4 1.136 17.9 -related Group B 1.000 46.2 1.205 47.9 1.115 48.9 1.205 50.5 N.A. Establishments Group C 1.000 10.2 1.120 9.9 1.087 9.8 1.133 9.5 Group D 1.000 24.8 1.103 23.6 1.057 22.8 1.126 22.0
National 1.000 100.0 1.893 100.0 2.340 100.0 1.847 100.0 Value of Group A 1.000 9.5 2.020 10.2 2.353 11.1 2.157 12.9 Shipments Croup B 1.000 70.0 1.889 69.8 2.313 69.0 1.725 64.4 N.A. Group C 1.000 10.4 1.809 9.9 2.262 9.6 2.035 10.6 Froup D 1.000 10.1 1.889 10.1 2.395 10.4 2.156 12.1
Note For Manufacturing and Service-related Establishments, the data are only available for the years of 1966 and 1969 Instead of 1965 and 1970.
Servlces-related establishments Include wholesale and retail trade, finance and Insurance, real estate, transport and communication, electricity,. gas.and water, and services including medical and other heakth services and educational services. Sources: Japan Statistical Yearbook, 1963, 1967, 1972, and 1978/Minryoku, 19D1. 48
increased from 58.0 to 61.5 percent and 46.2 to 47.9
percent, respectively. The value of shipments category
went down slightly from 70.0 to 69.8 percent, but still
absolutely dominated others. The most noteworthy
phenomenon in this term was a sharp drop of the DID
population in Group D, from 24.3 to 22.2 percent.
During this term, in 1964, the Tokaido Shin Kansen
was built between Tokyo and Osaka.
2. During the second five years (1965-70), the dominance
of Group B had not changed, but the growth of its
share slowed down. The share of the DID population,
in particular, slowed down from 4.4 percent growth
during the previous term to 2.2 percent during this
term. Group B's shares in manufacturing establishments
and the value of shipments also dropped, but only
slightly. Group D ’s shares in prefectural population
and DID population had fallen again, fairly sharply.
During this term, the construction of the Sanyo Shin
Kansen was underway.
3. During the first five years of the 1970's (1970-75),
Group B still dominated the others, but the growth of
its share considerably slowed down. The increase of
its share in the DID population category was only
0.2 percent. Even in the prefectural population
category, the growth of Group B's share dropped from
2.8 percent during the previous term to 1.4 percent 49
in this term. The most significant change occurred
in the category of the value of shipments of Group B
which dropped from 69.0 percent share during the
previous term to 64.4 percent in this term. During
this term, the Sanyo Shin Kansen was completed between
Osaka and Fukuoka, and the construction of the Tohoku
and Joetsu Shin Kansens started.
4. For the second half of the 1970's (1975-80), only the
data for prefectural population are available, and for
the first time, Group A increased its share. Group B's
share, on the other hand, increased very slightly,
only 0.3 percent. Group C maintained its share of
9.3 percent and Group D's share continued to drop,
but its rate of decline slowed down considerably.
From the above observations, however, it is hard to specify a strong relationship between the phased development of the Shin Kansens and regional development, except for the following two phenomena which might be considered to be the results of the creation of the Shin
Kansens in Japan. These are:
1. Group B (the prefectures along the Tokaido Shin Kansen)
has continuously grown during the past two decades;
2. Group D (the nineteen predectures which have had
nothing to do with the Shin Kansen) has continuously
declined in terms of the size of its population. 50
Along with the above analysis, the movement of the
center of gravity of population in Japan between 1960 and
1980 was investigated (Figure 7). As shown in the enlarged
portion of Figure 7, the center of gravity made the largest move toward the east between 1965 and 1970, then changed direction slightly to the southeast, and during the last
five years (between 1975 and 198 0), it moved upward to the northeast again. These moves clearly indicate the rapid increase of population in the Tokyo area (3.15 million) in the early 1960's and the continuous increase in the
Tokaido area (0.8 9 million) and the sharp increase in the
Sanyo area (0.25 million compared with 0.3 million decline in the previous term) in the late 1960's, and then, the sharp increase in the Sanyo area (0.68 million) and in the
Tohoku area (0.88 million) in the early 1970's, and the continuous, and only absolute, increase in the Tohoku area
(0.98 million) in the late 1970's (Table 2). The trends of population movement shown in Figure 7 and Table 2 are suggestive that there could be fairly strong relationship between the development of the Shin Kansens and the
Japanese development process, but are not perfectly conclusive.
The above analyses, based on data at the prefectural level, may seem rather too broad for examining the impacts of the Shin Kansens in the development process. So, the next study narrows down the scope of the research to a ** 1 3 J.
18
17
16 Sappc ro 15
14
1980(6.87,7]54K A one
12 1960 ■ Moficka
Sencai 10
9
Tokj 8
7 ------> u r--- waflgyi -- f— ityoti o p iJhjytt oka
Kobe f ^l a a k i . ^ ^ manatsu 6 T k i j Hire *fmn% /f 5
Fukue
3
0 1 2 3 4 5 6 7 8 9 10 11
Figure 7.— Center of Gravity of Population in Japan (196 0-1980). TABLE 2.— Population Changes in Five Regions (1960-1980). Unit: 1,000s
I960 1965 1970 1975 1980 Actual Actual Change Actual Change Actual Change Actual Change
Tohoku Region 19,289 19,320 31 19,432 112 20,307 875 21,289 982
Tokyo Metro, Reg. 17,864 21,017 3,153 24,113 3,096 27,067 2,954 28,694 1,627
Tokal Region 10,928 11,779 851 12,668 889 13,712 1,044 14,396 694
Osaka Metro. Reg. 13,189 14,923 1,734 16,510 1,587 17,845 1,335 18,442 597
Sanyo Region 9,462 9,435 -27 9,681 246 10,298 617 10,751 453
Note: Concerning Che configuration of the above region, see Figure 5.
Tohoku Region: 9 prefectures (Group A) Tokyo Heto. Reg. (Tokyo Metropolitan Region): Tokyo, Saltama, Chiba, and Kanagawa prefectures. Tokai Region: Shizuoka, Alchl, Clfu, and Hie prefectures Osaka Metro. Reg, (Osaka Metropolitan Region): Osaka, Kyoto, and Hyogo prefectures, Sanyo Region: Okayama, Hiroshima, Yamaguchl, and Fukuoka prefectures.
Source: Aaahl Shlmbun Sha (The Asahl Newspaper Publisher), Mlnryoku 76 and 81 (Prefectural Powers and Resources. Tokyo: The Asahl Newspaper Publisher. (In Japanese) 53 smaller scale (the city level). A total of sixty cities were selected and divided into six groups. Group A consists of four cities on the Tokaido Shin Kansen route where HIKARI (the fast train) stops. The second group,
B, consists of nine cities on the Tokaido Shin Kansen route where KODAMA (the slower train) stops. Group C is composed of four cities on the Sanyo Shin Kansen where
HIKARI stops. Group D is composed of ten cities on the
Sanyo Shin Kansen route where KODAMA stops. Group E consists of fourteen cities along the forthcoming Tohoku and Joetsu Shin Kansen routes, and the final group, P, is made up of nineteen prefectural capitals where the service of the Shin Kansen system will not be expected in the near future.
The population data for these sixty cities during the term between 1960 and 198 0 are listed in Table 3.
Between 1960 and 1965, the largest growth rate was recorded by Group A's DIDs (1.375) and the smallest increase also recorded by Group A's cities. This clearly indicates that a rapid suburbanization occurred during this term, and this trend has continued until today.
The second largest increase was recorded by Group E
(1.328), but this sharp increase was due to an extraor dinary increase achieved by two cities, Koriyama (2.175) and Oyama (2.591). These two cities are located in the area just north of the Tokyo metropolitan area, and a 54
TABLE 3.— Population Changes in Sixty Cities (1960-1980).
Growth Growth Growth Growth Population Rate Rate Rate Rate 1960 1960/65 1965/70 1970/75 1975/80
Tokyo 1^ 8,310,027 1.070 0.994 0.978 0.946 Nagoya 1,591,935 1.216 1.052 1.021 1.002 Kyoto 1 1,284,818 1.062 1.040 1.030 0.993 Osaka 1 3,011,563 1.048 0.944 0.932 0.928 < Average Growth Rate 1.099 1.008 0.990 0.967 a o M Tokyo-DID 8,688,599 1.252 1.098 1.037 U Aichi-DID 2,154,613 1.427 1.166 1.183 Kyoto-DID 1,298,965 1.258 1.129 1.119 N.A. Osaka-DID 4,387,952 1.564 1.230 1.119 Average Growth Rate 1.375 1.156 1.115
Yokohama 1,375,710 1.300 1.251 1.171 1.051 Atami 52,163 1.046 0,940 1.003 1.005 M Odawara 124,813 1.149 1.093 1.108 1.012 a Shizuoka 328,819 1.118 1.132 1.073 1.023 3 O Hamamatsu 333,009 1.179 1.101 1.085 1.042 ha O Mlahlma 62,966 1.131 1.097 1.142 1.053 Toyohashi 215,515 1.107 1.083 1.101 1.057 Gifu 304,492 1.176 1.077 1.060 0.995 Otsu 113,547 1.066 1.419 1.115 1.090 Average Growth Rate 1.141 1.133 1.095 1.036
U Okayama 260,773 1.119 1.285 1.369 1.055 o Hiroshima 431,336 1.169 1.075 1.573 1.019 3 O Kitakyushu 986,401 1.057 1.000 1.015 0.999 h* 5J Fukuoka 1,047,122 1.159 1.138 1.175 0.976 Average Growth Rate 1.126 1.125 1.783 1.012
Kobe 1,113,977 1.092 1.059 1.056 0.989 Akashi 129,780 1.227 1.296 1.137 1.070 Himeji 328,689 1.119 1.110 1.068 1.015 aAioi 36,521 1.066 1.113 1.150 0.986 Kurashiki 125,097 1.155 2.352 1.156 1.028 3a a Fukuyama 140,603 1.210 1.499 1.293 1.047 U Hihara 80,395 1.022 1.005 1.013 1.024 Iwakuni 100.346 1.056 1.002 1.047 :..014 Tokuyama 77,246 1.096 1.163 1.086 1.033 Shinonoeeki 246,941 1.030 1.016 1.032 0.985 Average Growth Rate 1.107 1.262 1.104 1.019
Mote: 1 Ku pare - Ku is similar jurisdiction to Ward. Ku pare may be understood as a city part compared with DU) part which may be regarded as a metropolitan part.
- over - al 3—Continued. 3.— Table
ore TeJpnSaitcl erok. 93 16. 92 1978. 1972. 1967. 1963. Yearbooks. Statistical Japan The Source: Group F Group E Hlyazaki Matsuyama Tokushima Matsue ia124,807 Oita Akita otr 104,833 Takamatsu Tottori Fukui Toyama Nagano Yamagata ym 34,973 181,937 Haebashi Oyama Utsunonlya igt 314,523 Niigata Takasaki Kumamoto Nagasaki Kochi Kagoshima ou160,963 298,972 Kanazawa Kofu Nagaoka 157,441 Fukushima Morioka Aomori aa129,888 Saga ahnh 174,348 Hachinohe Koriyama aoae243,012 Hakodate Sapporo Sendai Average Average Asahi Shimbun, Mlnrvoku 81. Mlnrvoku Shimbun, Asahi Growth Rate Growth whRt 1.328 Rate owth G Population 1960 7,2 .8 1.081 1.089 373,922 296,003 238,604 182,7B2 207,266 344,153 228,172 158,328 196,288 188,597 203,661 425,272 106,476 149,823 239,007 142,152 202,211 523,839 160,522 148,254 0,3 2.175 102,636 138,961 55 rwhGrowth Rate Growth 1.133 .7 .3 1.069 1.061 1.039 1.814 1.066 1.178 1.036 1.110 1.110 1.155 .6 .2 1.090 1.127 1.067 1.038 1.057 1.038 1.132 1.123 1.071 1.077 1.027 1.064 1.157 1.092 2.591 1.117 1.133 1.223 1.185 1.044 .3 .3 1.129 1.133 1.131 1.124 .1 .7 1.101 1.070 1.086 1.110 1.002 1.517 1.250 1960/65 Rate .0 1.155 1.109 1.151 .0 1.168 1.104 .9 1.272 0.993 .2 .3 1.0B7 1.107 1.133 1.134 1.228 .3 1.081 1.039 .5 1.071 1.156 1.068 .4 1.138 1.142 .5 1.061 1.059 1.651 1.054 1.089 .7 1.071 1.176 .1 1.095 1.078 1.110 957 1970/75 1965/70 1.182 .7 1.094 1.077 1.076 1.123 .4 .5 1.029 1.121 1.058 1.127 1.049 1.108 .0 1.075 1.103 .7 .2 1.092 1.228 1.271 1.162 1.084 1.134 1.033 1.310 1.080 Growth aeRate Rate 1.108 1.229 1.109 1.154 1.102 1.075 1.077 1.142 1.143 1.095 1.103 Growth 0.983 1.090 1.090 1.043 1.030 1.030 1.049 1.076 1.056 1.079 1.044 1.052 1.046 1.034 1.025- 1.021 1.040 1.060 1.054 1.050 1.025 1.055 1.077 1.034 1.056 1.078 1975/80 1.047 1.054 1.082 1.052 1.047 1.031 56 number of industrial plants which were forced to move out of the Tokyo area moved into these two cities. Without these two cities, Group E ’s growth rate drops to 1.152.
During the second tern, between 1965 and 1970, Group D experienced the highest growth (1.262). Kurashiki City greatly contributed to this growth with a growth rate of
2.352. Fukuyama City also contributed greatly (1.499).
The reason for the rapid expansion of Kurashiki may be explained partly by the creation of the Sanyo Shin Kansen and partly by the development of a huge steel plant in
Fukuyama City along with petro-chemical plants around the area. Kurashiki is a beautiful, historic town, and it obviously attracts newcomers for the recently developed plants described above. Okayama City in Group C also experienced very high growth (1.285), presumably in anticipation of the creation of the Sanyo Shin Kansen within a few years. During the third period, 197 0 and
1975, the highest growth was experienced by Group C, especially the largest city in the group, Hiroshima
(1.573). Okayama, Fukuyama, Kurashiki, and Fukuoka, all in the groups C and D, experienced very high growth.
During this term, the first phase of the Sanyo Shin Kansen between Osaka and Okayama was completed in 197 2, and its extension to Fukuoka was completed in 1975. During the same term (1970-75) , the city part of Group A experienced a decline. The second highest growth in this term was 57
experienced by Group E which expects the creation of the
Tohoku and Joetsu Shin Kansens within a few years. This
trend became much clearer during the fourth term, between
1975 and 19 80. For the first time, Group E's growth
dominated the others. Group A experienced more drastic
decline during this term. The population growth in
Groups B, C, and D stabilized. Notably, Group F climbed up to second for the first time.
Several studies reveal the impacts of the Shin
Kansen on regional development very clearly. For instance,
according to the research done by the Japanese Development
Bank, during the six years between 1964, when the Tokaido
Shin Kansen was built, and 1972, when the Sanyo Shin Kansen
was extended to Okayama, the number of branch offices or
liason offices established by large enterprises increased
to 378. Up until 1964, the total number of such branch
and liason offices in Okayama was 202. 3 9 Also, during the
two years of 1975 and 1976, according to the research done
by Unyu Chosa Kyoku (the Transportation Research Bureau), the number of hotels in Fukuoka increased 100 percent,
from 20 to 40, and in Hiroshima, 300 percent, from 4
39The Japanese Development Bank, The Impacts of the Sanyo Shin Kansen on Regional Society, May 1975 cited by Zai Matsumura of the JNR's Hiroshima Branch, We Are Now Approaching the Completion of the Sanyo Shin Kansen (Address before the members of the Hiroshima Chamber of Commerce), February 24, 1975. (mimeo in Japanese). 58
to 13. Rental office buildings were also built in and
around the Shin Kansen terminal in the above cities.40
As a matter of fact, the so-called redevelopment project
in and around the Shin Kansen terminal site was commonly
seen in most of the cities along the Tokaido and Sanyo Shin
Kansens. Even in such cities as Sapporo, Sendai, Niigata,
and Morioka, where the service of the Shin Kansen is
expected within a few years, similar trends (redevelopment
projects in and around the site for the prospective Shin
Kansen terminal) are commonly seen.
From these two analyses (prefectural level analysis
and city level analysis), what kind of conclusion should
be drawn? Are there really strong relationships between
the phased development of the Shin Kansen and regional
development in Japan? Some authorities stress that the
industrial redistribution policies represented by the
"New Industrial City" and the "Pacific Coast Belt Industrial
Zone," both implemented in 1963, have been gradually
showing their effects. However, no data support this
assertion. Only 1 out of 15 designated new industrial
cities, for instance, has grown more than the national
40Unyu Chosa Kyoku, The Extension of the Sanyo Shin Kansen and Its Effects on Regional Society, March 1976 cited by Unyu Keizai Kenkyu Senta (Transportation Economy Research Center), Research concerning the Improvement Methods for Trunk Express Transportation Systems, March 1980, p. 137. 59
average of urban growth during the past two decades.^
Nevertheless, it is still not certain to conclude that
there is a strong relationship between the phased develop
ment of the Shin Kansen and regional development in Japan
until several other factors are considered. Those factors
are:
1. strict environmental regulations assigned by local
governments in the old industrial zone areas have
spurred the outflux of industrial plants to economically
underdeveloped regions such as the Tohoku and Kyushu
regions;
2. growth management policies represented by the restric
tion for new construction of office buildings, colleges
and universities, and plants in such cities as Tokyo
and Osaka have pushed some business activities and
students to local areas;
3. sky-rocketing land costs in large cities have impeded
the necessary expansion of plants and industries and
have eventually pushed out business activities from
those cities;
4. the conversion of the energy base from coal to oil
and natural gas has required a huge storage capacity,
and this has accelerated the new construction of port
41 The urban population in Japan had increased 5 6.3 percent more from 4 0.8 million in 1960 to 63.8 million in 1975. 60
and storage facilities in relatively less populated
areas. In fact, since the early 1960's, the so-called
"four old industrial zones" which had dominated
Japanese industrial activities have lost their status.
The sharp reductions of the value of shipments in the
prefectural group, B, during the past two decades
(which are illustrated in Table 1) are largely due
to the decline of the old industrial zones (Figure 8);
5. even the aforementioned government's industrial
distribution policy has been behind the movement
of population during the last two decades.^
While it is true that these five factors have multiplied the effects of the Shin Kansen system on regional development, it has been broadly said that the development of the Shin Kansen system between Tokyo and
Fukuoka has unified the southern half of the Honshu Island economically. It may be theoretically demonstrated that economic development is propagated circularly and cumula tively along the major transportation routes joining leading urban-industrial centers as just seen in the
42 The Japanese government designated 13 new industrial cities in 1963 and later added two more. Except for a few cities, most cities have been growing less than originally expected. The detailed study concerning new industrial cities was done by Norman J. Glickman. See Norman J. Glickman, The Growth and Management of the Japanese Urban System, N.Y.: Academic Press, 1979 and Shun1ichi Hagiwara (1977) , pp. 108-110. For details of the Pacific Coast Belt Industrial Zone, see Shun'ichi Hagiwara (1977), pp. 11, 42-46. 61
Group A
Group C
Old Industrial Zona
Group D Group A
Sendai
y a hama Keihin ] Group D[ Industrial Zone
Kitakyushu |Group C Chukyo 1 Group B| Industrial Industrial Zone Zone Hlros
Group C Hanshin Industrial Zone
Group D
Figure 8-— Four Old Industrial Zones in Japan. 62
economic development process along the Tokaido and Sanyo
Shin Kansens explained above. Pottier, for instance,
stressed the self-generating features of modern trans- 4 3 portatron system as follows:
1. the appearance of initially developed routes of modern
transportation encourages interurban, or interregional,
trade between the cities served;
2. the growth of traffic between these cities yields
scale economies and lower per unit transfer costs;
3. the resultant, reduced shipping rates further stimulate
interurban trade;
4. increased trade creates a demand for new transport
facilities and provides the capital for such
improvements;
5. repeated iterations of this sequence attract economic
activities and population to the transport services
and product markets paralleling the original major
routes, and particularly to those large centers
located at the most nodal route intersections.
Although the Shin Kansen system was designed to
serve passenger operation exclusively, it is safe to say that the system has generated not only extensive amounts
^P. Pottier, "Axes de communication et theorie de developpement,” Revue Economique, Vol. 14 cited by Alan Pred and Gunner Toernqvist, Systems of Cities and Information Flows. Lund Studies in Geography, Series B, Human Geography, No. 38, 1973, p. 57. 63
of the human transactions but also extensive amounts of
interurban and interregional trade along its route. It
should not be unreasonable or invalid to conclude that some definitely positive effects of the Shin Kansen have contributed to the economic development process in Japan during the past two decades.
Role of High-Speed, Intercity Passenger Train Systems on Human Contacts and Transactions
A number of research investigations undertaken in
Japan verify the significant role played by the Shin Kansen on human contacts and transactions. For instance, according to survey research done by the Hiroshima Chamber of Commerce, the following impacts are considered to have been brought on by the creation of the Sanyo Shin Kansen 4 4 to the Hiroshima area. These are:
1. business communication and contacts became extremely
easy. (More than 6 0 percent of the respondents pointed
out this merit.);
2. human flow between Hiroshima and such big cities as
Tokyo, Osaka, and Fukuoka increased dramatically,
especially the flow between Osaka and Hiroshima.
(According to the JNR's data, passenger travel
44 Hiroshima Chamber of Commerce Union, The Investiga tion concerning the Impacts due to the Opening of the Sanyo Shin Kansen on the Region, (in Japanese), March 1976 (mimeo). 64
between Hiroshima and Osaka increased by 32 percent
between 1974 and 1979.) (About 40 percent of the
respondents pointed out this merit.);
3. collection of information became very easy (25 percent);
4. competition among businesses became more intensive
(about 20 percent);
5. market ranges increased dramatically (15 percent);
6. purchase of goods became easier (5 percent);
7. land price and wages increased (5 percent).
The top three impacts revealed in the response are related
to human contacts. Similar research was done by the
Kyushu Economic Survey Association (KESA) in 1976, and
the result was almost the same as the one done by the
Hiroshima Chamber of Commerce described above. According 45 to the KESA's report, the number of passengers between
the Kyushu area and the Tokyo area increased approximately
2 8 percent and to Osaka by 59 percent in the year of 1975.
These figures, however, need to be discounted considerably because the total patronage of the Shin Kansen has dropped
rather sharply from 157 million in 1975 to 124 million 4 6 both in 1978 and 1979. The reasons for this sharp drop
4 5 Kyushu Economic Survey Association, The Extension of Shin Kansen and Kyushu Economy, (in Japanese), 1976 (mimeo). 46 The Ministry of Transportation, Japan, ed., The 198 0 White Paper of Transportation, November 198 0, Tokyo: The Ministry of Finance Printing Office, p. 326. 65
in patronage of the Shin Kansen are many. Some attributed
the decline to the long-standing sluggish economy in the
area since the 197 3 Arab Oil Embargo, but the sharp
increase of air passengers from 25 million to 41 million
in 1979 (64 percent up) and the increase of automobile
traveller from 17,681 million in 1975 to 23,405 million
in 1979 (33 percent up) do not justify the above claim.
One reason for this drop in ridership could be the sharp
increases in fares of the Shin Kansen to make up the deficit from local operations; in fact, the total number of JNR passengers went down only 1.6 percent between 1975 and 1979. Another reason could be the introduction of the so-called "Wide-body Jet," such as the Boeing 747 and
DC-10, into the long distance market by the Japan Air Line
(JAL) and All Nippon Airways (ANA). Tables 4 and 5 and
Figures 9 and 10 clearly illustrate the state of affairs described above.
Table 4, for instance, shows the transition of the fares of the Shin Kansen and the above two airlines. Note that, up until 1975, the fare of the Shin Kansen was considerably lower than that of the air mode. In 1975, a regular ticket of the Shin Kansen was less than half of the air fare both for the Tokyo-Osaka and Tokyo-Fukuoka routes. The sharply raised rail fare in 197 6 changed the situation rather drastically. For the Tokyo-Osaka route, for instance, the air fare became only 9 dollars higher TABLE 4.— The Pares of the Shin Kansen and Air Modes. Unit: Yen
Route 1970 1975 1976 1977 1978 1979
Tokyo - Osaka
Shin Kansen 4,130 5,010 8,300 8,300 9,300 9,500 n.a. n.a. 14,300 12,300 13,300 13,500 (a green tlcket/lst class)
Air Kode 6,800 10,600 10,400 10,400 10,400 14,400
Tokyo - HaVata
Shin Kansen 5,360 8,710 14.600 14,600 15.300 15.500 n.a. n.a. 23.600 20,000 21.300 21.500 (a green ticket/1st class)
Air Mode 13,800 20,100 20,100 20,100 20,100 25,900
Source; Unyu Kelzai Kenkyu Senta (Transportation Economy Research Center), Research Report concerning the Improvement Hcthods for Trunk Express Transportation System (in Japanese), p. 53. 67
than the rail fare, which was about 38 dollars at that
time. The rail fare was raised again in 1978, and the
difference between the rail fare and the air fare narrowed more (only 4 dollars for the Tokyo-Osaka route). In 1979, however, the situation again changed considerably. The airlines were obliged to raise their fares rather drastically; the air fare for the Tokyo-Osaka route rose
38.5 percent and for the Tokyo-Fukuoka route 28.9 percent.
As a result, for the first time since 1975, the price of a regular ticket of the Shin Kansen became considerably lower than the air fare, and even a first class ticket of the Shin Kansen became less expensive than the air fare by about 50 cents. The impact of this sharp rise of air fares has not been computed yet; however, it will be not so crucial for the air mode, because the JNR, as the national railway corporation, has been, and will be, assigned to serve local areas even though the necessary services are likely to be unprofitable. As a matter of fact, the Shin Kansen system has provided almost 4 0 percent of the total revenue of the JNR, but the distance covered by this system is only 5 percent of the total distance operated by the JNR. The result has been, and will likely be, an annual increase of the rail fare. Such increases steadily lessen the competitive ability of the JNR against the air mode. 68
Figure 9 illustrates the issue described above graphically. Six variables (the number of automobiles, wages, the JNR's rail fare, the consumer price index, the price of automobiles, and the air passenger fare) are selected and compared. As shown, the JNR's rail fare rose more sharply than the consumer price index since 1975 and more than the price of automobiles and air passenger fares since 1974. As a matter of fact, air passenger fares rose only 194 percent since 1967, even after the sharp rise of the fares in 1979, compared with the rail fare which rose 320 percent during the same term. The fare relationship between the rail mode and air mode, however, would be unpredictable; while the JNR's rail fare might be subject to a continuous raise, the air fare also would be quite vulnerable to a continuous hike in the price of crude oil. Local air services could become less and less profitable.
Beside the issue of rail and air fares, the most noteworthy trend observed in Figure 9 is the sharp and constant increase in the number of automobiles since 1967.
This extremely sharp increase in the number of automobiles
(655 percent since 1967) is obviously owing to two facts: one is the sharp increase in wages during the same term
(509 percent) and the much slower increase in the price of automobiles during the same term (194 percent). In fact, the share of the automobile mode of transportation in the 69
655 The Humber of Automobile (Index) 600
Wage 509 500
400
JNR's Rail Fare 320 300
261 Consumer Price Index (National)
200 Price of Automobile
Air Passenger Fare
100
1967 1969 1971 1973 1975 1977 1979 1968 1970 1972 1974 1976 1977
Figure 9.— Transition of Wage, Number of Automobile, Price of Automobile, Consumer Price Index, Air Fare, and Rail Fare in Japan.
SOURCE: The Ministry of Transportation, ed. , 198 0 Transportation White Paper, p. 134. 70 national total of transportation output in terms of passenger-km has constantly increased, from only 4.0 percent (8.8 billion passenger-km) in 195 9 to 41.1 percent
(319.9 billion passenger-km) in 1979. During the same term (between 1959 and 1979) the national output increased
350 percent from 221 billion passenger-km in 1959 to 777 billion passenger-km in 1979, but the automobile mode's output easily surpassed the rate of the national increase with 1,60 0 percent. Incidentally, the share of the air mode was 3.9 percent (30.3 billion passenger-km) in 1979 and the Shin Kansen Share was 5.3 percent (41 billion passenger-km) in 1979. Although the share of the air mode is still low, the growth rate of the air mode since 195 9 is a striking 6,200 percent. The Shin Kansen, on the other hand, has been maintaining, more or less, 5 percent of its share in the national total since 1969 (Table 5).
Although the sharp increases of the ridership in both the automobile and air mode during the past two decades cannot be ignored, after 15 years of Shin Kansen's operation, the shares of each transportation mode in the
Japanese market quite likely has become fixed. Figure 10 illustrates the shares of four different modes of trans portation such as air, rail, automobile, and marine in five different markets in three different periods. As clearly shown, the automobile mode governs the market up to 300 km (approximately, 200 miles) and the rail mode TABLE 5.— 1959-1979 Domestic Share by Seven Different Transporta tion Modes. Unit: million passenger-km {percentage}
National Railways Private Output Total Shin Nansen Railways Bus Autouoblle Air Waterborne
_ 1959 221,292 114,189 56,113 39,180 8,820 489 2,500 (100.0) (51.6) (0) (25.4) (17.7) (4.0) (0.2) (l.l) 1969 528,813 181,520 22,816 93,804 100.192 141,869 6,991 4,439 (100.0) (34.3) (4.3) (17.7) (18.9) (26.8) (1.3) (0.8) 1970 587,178 1B9.726 27,890 99,090 102,894 181,335 9,319 4,814 (100.0) (32*3) (4.7) (16.9) (17.5) (30.9) (1.6) (0.8) 1971 617,848 190,321 26,795 99,719 100,843 211,635 10,304 5,026 (100.0) (30.8) (4.3) (16.1) (16.3) (34.3) (1.7) (0.8) 1972 648,188 197,829 33,835 102,469 108,211 220,346 12,663 6,670 (100.0) (30.5) (5.2) (15.8) (16.7) (34.0) (2.0) (1.0) 1973 674,133 208,097 38,989 104,831 111,713 225,732 16,035 7,724 (100.0) (30.9) (5.8) (15-6) (16.6) (33.5) (2.4) (1.1) 1974 693,596 215,564 40,671 108,460 115,776 228,400 17,639 7,756 (100.0) (31.1) (5.9) (15.6) (16.7) (32.9) (2.5) (1.1) 1975 710,711 215,289 53,318 108,511 110,063 250,804 19.14B 6,895 (100.0) (30.3) (7.5) (15.3) (15.5) (35.3) (2.7) (1.0) 1976 709,549 210,740 48,147 106,826 98,714 264,499 20,119 6,651 (100.0) (29.7) (6.8) (15.3) (13.9) (37.3) (2.8) (0.9) 1977 711,033 199,653 42,187 112,644 104,639 263,961 23,636 6,500 (100.0) (28.1) (5.9) (15.8) (14.7) (37.1) (3.3) (0.9) 197B 747,489 195,844 41,074 115,285 107,009 296,043 26,923 6,384 (100.0) (26.2) (5.5) (15.4) (14.3) (39.6) (3.6) (0.9) 1979 777,336 194,690 40,986 117,770 108,317 319,869 30,246 6,443 (100.0) (25.0) (5.3) (15.2) (13.9) (41.1) (3.9) (0.8)
Source; The Hlnlstry of Transportation, Japan, The 1980 White Paper of Transportation, p. 326. 72
100-300 km 300-500 km Waterborne 9 .0 water Auco borne xSRLtt ’■■■ Air 1965 44.2 2§gl2. Es-, 2.0
* f a 6
1971 56.6 ■ 2 1 .( 2.9
-•'7.9
1978 72.3 26.2 5.4
Water 500-750 Van Auto borne 750-1,000 km Auto Waterborne 0.7 5.6
1965
1971
1978 3 i;'4 :
1,000 km & Over Waterborne 0.3
1965 Source: The Ministry of Transportation, Japan The 1980 White Paper of 1971 Transportation, p. 139.
1978
Figure 10.— Share of Four Transportation Modes for Five Different Ranges. 73
governs the market between 300 and 1,000 km (200 to 625
miles). Beyond 1,000 km, the air mode strongly governs
the market with more than 65 percent of the share.
Throughout the analyses of the data observed in
Tables 4 and 5 and Figures 9 and 10, it has become clear
that the Japanese HSIPT system, the Shin Kansen, will
continuously capture the patronage in the market from
200 to 600 or 700 miles, and its share in the national
market would be more or less 5 percent. A consistent
. contribution to the increase of the ridership, however,
can be expected from the completion of both Tohoku and
Joetsu Shin Kansens in 1984.
Through the previous analysis, it has become clear
that the Shin Kansen has been accommodated in the Japanese
transportation system as a key channel for human contacts
and transactions, especially in the market range between
200 and 600 miles. In the following text, the nature and
characteristics of the patrons of this system will be
investigated. This investigation is necessary to examine
Thorngren's proposition that the contact work will mainly
be performed by people in higher positions and by
professionals.
Recently (1977), the JNR made a survey concerning 47 the characteristics of the patrons of the Shin Kansen.
47The JNR, The 1977 Survey Concerning the Characteris tics of Passengers of the Shin Kansen. (in Japanese), (mimeo). 74
The following sections are summaries of data tabulated by the JNR and released by it:
Purpose of trips - Among 42 3,000 passengers who took the
Shin Kansen on October 13, 1977, 298,676 (70.6 percent)
took HIKARI (the fast train which stops only at large
cities such as Tokyo, Osaka, Nagoya, Kyoto, Hiroshima,
and Hakata, which is the name of the station for the • * City of Fukuoka), and the rest, 124,623 (29.4 percent),
took KODAMA (the slower train which stops at every
station along the Shin Kansen routes). Incidentally,
HIKARI means LIGHT in English and KODAMA means ECHO.
Seventy-seven percent of HIKARI passengers and almost
80 percent of KODAMA passengers claimed their trips
were business-related.
Reasons for using the Shin Kansen - For HIKARI passengers,
SAFETY came first and FASTNESS and RELIABILITY came in
second and third. For KODAMA passengers, FASTNESS came
first, and SAFETY and RELIABILITY came second and third.
These are very interesting responses. For HIKARI
passengers who travel between large cities, the air
mode is still faster than the Shin Kansen mode, but
they esteem the Shin Kansen's achievement in safety
greatly. For KODAMA passengers who travel between a
large city and smaller cities, or between smaller cities,
the reduction of time-distance due to the creation of
the Shin Kansen must have been tremendous compared to 75
the service offered by the old rail system or by the
automobile. The differences found in the responses
from HIKARI and KODAMA passengers should be understood
in such a manner. Other reasons pointed out by both
HIKARI and KODAMA passengers are: the appropriateness
of the DISTANCE traveled by the Shin Kansen mode,
LOWER FARES compared with other modes of transportation
{keep in mind, the cost of gasoline, which has been
approximately $2.50 per gallon, is much higher than
that in the United States), COMFORTABLENESS, DESIGNATED
MODE OF TRANSPORTATION by employer, GOOD FOR GROUP
TRAVELLERS, and NO OTHER TRANSPORTATION MODES AVAILABLE.
The number of days spent, or will spend, for the trip -
For HIKARI passengers, three days comes first (2 8.7
percent), then, two days (26.9 percent), and between
four and six days (22.3 percent) follow the three day
trip. 11.7 percent of HIKARI passengers claimed a
single day trip. For KODAMA passengers, two days comes
first (32 percent), then one day (29.4 percent), and
three days (19 percent) follow the two day trip. From
these data, it is clear that KODAMA passengers spend
lesser days than HIKARI passengers for their trips.
Further, 67.3 percent of HIKARI passengers and 80.4
percent of KODAMA passengers spent less than four days
for their trips. 76
When the trip was planned or decided - For HIKARI passengers/
two to seven days before the trip comes first (27.8
percent), then eight to fourteen days comes second
(15.3 percent)/ and a month before comes third (15.1
percent). Twelve percent of HIKARI passengers decided
their trips a single day before and 7 percent decided
their trips on that particular day. For KODAMA
passengers, 55.5 percent decided their trips less
than a week before. The data shown here clearly
indicate the characteristics of the Shin Kansen.
The Japanese Shin Kansen is the mode of transportation
that is always available for the travellers; in other
words, CONVENIENCE will always be one of the strong
characteristics of the Shin Kansen. This characteristic
is strongly supported by the fact that almost twenty
percent of the HIKARI passengers and thirty percent of
KODAMA passengers planned or decided their trips either
a single day before the date of trip or on the day of
the trip itself. This characteristic of the Shin Kansen
will also be supported by the next piece of data.
When the ticket was purchased - More than 5 0 percent
(51.1 percent) of HIKARI passengers and 40 percent of
KODAMA passengers purchased their tickets on the date
of the trip or a single day before.
Which age blocks use the Shin Kansen most - Among men, the
thirties is the largest block, then the forties and 77
twenties follow the thirties. Among women, the twenties
comes first, then the forties and fifties follow the
twenties. For HIKARI passengers, the twenties, thirties,
and forties age blocks occupy more than 70 percent
(71.9 percent) and for KODAMA passengers, the twenties,
thirties, and forties occupy approximately 7 0 percent
(69.9 percent). Further, 77.7 percent of HIKARI
passengers and 77.6 percent of KODAMA passengers are
male.
Occupations of the passengers - Employees of private
companies and public institutions occupy the biggest
category (53.5 percent). Executives of private
companies or public institutions occupy the second
(11.5 percent). The third biggest category was
housewives (11.4 percent). Self-supporting merchants
and manufacturers follow the housewives (5.2 percent).
Students come seventh, a mere 5 percent.
How many persons make a group for the trip - Companionless
passengers make up the highest group (4 6.7 percent in
HIKARI passengers and 4 4.4 percent in KODAMA passengers).
Two person groups come second (26.1 percent in HIKARI
and 24.5 percent in KODAMA passengers). 48
4 8 The writer's personal experience also supports the propositions explained by Thorngren. After the creation of the Shin Kansen between Tokyo and Osaka, business trips assigned to the writer increased dramatically. Between 1970 and 1973, when the Japanese economy flourished, the amount of business trips primarily to Osaka had reached 78
From these data, the profiles of the Shin Kansen
passengers are: men in the twenties, thirties, and forties
and women in the twenties, forties, and fifties. Occupa
tions include company or public institution employees or
executives in the men's block and company employees or housewives in the women's block. The days spent for the
trip are usually less than four days. The passengers highly esteem the performance of the Shin Kansen in terms of safety, speed, and reliability. Finally, most of the purposes for their trips are business-related. These characteristics of the Shin Kansen passengers are very much similar to the ones found in the Northeast Corridor's more than a hundred a year. This had never happened when these two biggest cities were connected with the time- distance of more than eight hours or even with six hours just before the Tokaido Shin Kansen was created. The reduction of travel time from six hours to three hours (presently, the Shin Kansen ties Tokyo and Osaka in three hours and ten minutes) has made it possible to attend afternoon meetings at the same day in different places more than 4 00 miles apart. More importantly, this system made it possible to finish a business trip in a single day. This saves a lot of expenses previously incurred by business trips. The amount of per-diem expenses necessary for a three-day trip were reduced to those for one-day. The accommodation cost was eliminated for a single-day business trip. Another experience of the writer reveals a significant effect of the increase of speed and capacity of this transportation system on face-to-face contacts. In 1972 and 1973, the writer was engaged in a tourism development project in Seoul, Korea. The succeeding introduction of the "Jumbo Jet (B-747)" to the Seoul-Tokyo route by the Japan Air Line and the Korean Air Line had made the travel between those two cities safe, easy, and frequent. As a consequence, the writer was obliged to travel to Seoul more than twenty times during those two years to attend meetings necessary for the project. 79
rail passengers. According to the report released by the 49 U.S. DOT, rail passengers on the Northeast Corridor are
profiled as: male/ age 25-44, married, $25,000 or more
annual income, college or graduate degree holders, engaged
on business trips, professional and technical employees,
and also professors and students. Although no income and
marital data are available in the JNR's study, there is
no doubt that there is a significant correlation between
the Shin Kansen passengers and the Northeast Corridor's
rail passengers. In both cases, fairly highly educated
people often use both rail systems and the purpose of
their trips is mostly business.
The data from this JNR Survey can be aggregated
and analyzed in a myriad of contexts which would lead to
different conclusions. Useful to the context of this
dissertation, the following conclusions are judged to be
highly significant.
Throughout this part of the research, it has
become clear that the Shin Kansen has greatly contributed
to intensifying human contacts, especially among profes
sional employees, businessmen, and executives. Those
human contacts performed by people in higher positions definitely stimulates the information exchange necessary
4 9 The U.S. Department of Transportation, Two-Year Report on the Northeast Corridor, Wash., D.C.: U.S. Government Printing Office, 1978, pp. 27-43. 80
for the innovation of technologies and the expansion of
markets. To be the key channel for the human contacts
which are indispensable for further expansion of economic
activities in any region or any country, a HSIPT system
must be planned by taking into account the following
points:
1. A HSIPT system should be planned to join medium and
large cities;
2. A HSIPT system should be planned to secure safety,
reliability, and speed to be competitive with other
modes of transportation such as automobile, bus, and
air; and
3. A HSIPT system should be planned to serve distances
between 300 and 1,000 km (200 to 600 or the maximum
of 700 miles).
Positive and Negative Aspects Inherent in the Development of a HSIPT System
The positive aspects of a HSIPT system may be
arranged as follows:
1. Primary impacts such as the reduction of time-distance,
the augmentation of transportation capacity, and the
derived impacts from the accompanying investment with
the construction;
2. Secondary impacts such as the conservation of energy
and the enhancement of the tourist trade; 81
3. Tertiary impacts such as the enhancement of social and
cultural motivation, human contacts, and the enlarge
ment of market spheres.
As can be seen, most of these positive aspects of
a HSIPT system have been discussed in the previous text.
Among these various positive impacts, a much more clearly
visible benefit due to the creation of a HSIPT system will
be its energy-saving (in more concrete term, petroleum-
saving) ability. As mentioned earlier, British Railways
decided to replace its diesel-powered HST system with an
electricity-powered AGT system primarily because of the
concern for the source of power, oil. In France, according
to TIME, the TGV uses little more energy than conventional
trains, and since much of France's electrical grid is
powered by hydroelectric or nuclear energy, the nation's
growing investment in electric railroads will help lessen 50 its dependence on imported oil. In Japan, according to
Takeshi Tamura, Director of the Japanese National Railways'
New York Office, the Shin Kansen has saved more than 51 40 million barrels of crude oil annually. For instance,
according to him, in 1977 it took 4.4 million barrels of
heavy oil to generate electricity for the Shin Kansen system. If the 124 million riders (the number of the
5°TIME, October 5, 1981.
C 1 The NGA, Committee on Transportation, Commerce and Technology (1981), Appendix III. 82
Shin Kansen patrons: the writer) had used automobiles, allowing 2.2 passengers per car and 14 miles per gallon, they would have consumed 20.6 million barrels of gasoline or 46.1 million barrels of crude oil. The difference is a savings of 41.7 million barrels of crude oil. As can be seen, the assumption behind this calculation is very simple and arbitrary; however, if electricity for a HSIPT system is produced from coal (such as is the possible case in the United States) or nuclear energy, the possible savings of crude oil can be substantial. In addition to this, if a HSIPT system can successfully reduce ten percent of the automobile ridership in intercity travel in the
United States, the impact of crude oil saving will be tremendous. Presently, 30 percent of the crude oil consumed in the United States is consumed by the automobile, and more than 85 percent of intercity travel is made by the automobile. 52
The negative aspects of a HSIPT system are roughly classified into two areas. One is the direct negative impacts inherent in the creation of a HSIPT system such as noise and vibration due to a high-speed operation. For instance, the JNR and Japanese government appropriated
38 billion yen ($190 million) in 1979 and 24 billion yen
52 National Transportation Policy Study Commission, National Transportation Policies Through the Year 20 00: Final Report, June 1979, Wash., D.C.: U.S. Government Printing Office. 83
($120 million) in 1980 to the residents along the Tohoku and Joetsu Shin Kansen routes as compensation for noise 5 3 and vibration. The other is the so-called "counter-flow effect" which has been seen in Japan since the completion of the Tokaido Shin Kansen. According to the writer's study, the subsidence of administrative functions of such large cities as Osaka (the second largest city in Japan) and Nagoya (the third largest) since they were joined to
Tokyo in 1964 has been conspicuous.5^ It has been broadly said that even the island of Kyushu's economy has been absorbed by the central economy represented by Tokyo's economic structure since the completion of the Sanyo Shin
Kansen.
In fact, the perception of a HSIPT system by people in large cities and in smaller cities are often remarkably in discord. In the Japanese experience, people in large cities such as Tokyo and Osaka regard the HSIPT system as a device for further concentration of various functions, such as financial, managerial, operational, administrative, and even technological. People in local areas, on the other hand, regard the HSIPT system as a device to accelerate the decentralization of the above functions.
53The Ministry of Transportation (1980) , p. 90. 54 Concerning the details of the "counter-flow effect" of the Shin Kansen, refer to Shun'ichi Hagiwara (1977), pp. 91-94. 84
This disparity emerges as an obvious difference in the evaluation of actual effects of the Shin Kansen. According to a survey done by the Hiroshima Chamber of Commerce, discouragement about the Shin Kansen is often seen among local people and businesses, especially retail businesses.
On the contrary, those businesses such as construction, manufacturing, wholesale, finance and insurance, and services which manage their businesses regionally or nationally regard the effects of the HSIPT system (the
Sanyo Shin Kansen in this case) fairly positively.
Some of the discouragement with, or dissatisfaction about, the effects of the Shin Kansen expressed by retail business are: 1) a sharp increase of competition with those fellow businesses from large cities (big capital) and 2) diversified demand from the customers. Structural adjustments are necessary for such changes, but it is extremely difficult to achieve for locally based businesses with limited capital or restricted sources of information. 55
In fact, the so-called big businesses generally evaluate the Shin Kansen highly as a key means to expanding their business activities to local areas, which usually takes the form of opening new branches or liason offices. These sorts of reactions, perceptions, and evaluations concerning a HSIPT system by various different groups should be taken
55 The Hiroshima Chamber of Commerce Union (1976), pp. 1-20. 85 into account by HSIPT system planners. Once knowing these, a system planner can guide, instruct, and help to convince the public not to overestimate or underestimate what are often unknown or unmeasurable impacts of any proposed
HSIPT system.
In this chapter, the nature and characteristics of
HSIPT systems were introduced. Foreign experiences with
HSIPT systems were described and examined. Various problems inherent in the creation of a HSIPT system were investigated. In the following chapter, the probable and possible impacts on forty local communities within of such HSIPT system, if it were to be created in the Great
Lakes Midwest Region, will be investigated. CHAPTER III
PROBABLE IMPACTS OP THE CREATION OF A HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM: THE GREAT LAKES MIDWEST REGION - A CASE STUDY
This particular chapter is divided into two parts.
Firsts the bases for the designation of the principal HSIPT system corridors which were assumed to be created in the
Great Lakes Midwest Region are introduced. Various studies implemented by the states and the U.S. Department of
Transportation (DOT) concerning the possible HSIPT system corridors in this particular region are examined. Secondly, the probable and possible impacts of the creation of a
HSIPT system in the Region on the forty local communities which are assumed to be joined by the system are also examined. The concepts of the "population potential" and
"population energy" to determine before and after indices of spatial isolation of the forty local communities are applied in this analysis.
Development of the Rationales for the Great Lakes HSIPT System Corridors
As mentioned in the introductory chapter, the installation of a HSIPT system has been hypothesized for the Great Lakes Midwest Region, as a basis for this research undertaking. Further, one of the HSIPT system
86 87
corridors was assumed to be located so as to join 17 U.S.
SMSAs and 3 Canadian CMAs in the Region; and the Lansing
metropolitan area was assumed to be one terminal point
on the Midwestern corridor system. The bases for these
two assumptions take into account the following three
studies done by the States of Michigan and Ohio and the
U.S. Department of Transportation.
As mentioned before, Michigan and Ohio have been
studying an intra-state HSIPT system independently.
Michigan, for instance, is studying four possible intra
state routes, from Detroit to Grand Rapids (Figure 11) and
now is in the process of working out an agreement with
Amtrak to provide daily round trip service from Grand
Rapids to Chicago. 5 6 A second study is one developed
already by Ohio with a detailed plan for an intra-state
HSIPT system as shown in Figure 12. A third study is one
by the U.S. DOT, which had studied a HSIPT system a decade
ago and released a plan for a possible High-Speed Ground
Transportation (HSGT) network in 1973 (Figure 13). Taking
into account the above three studies and the location of
urban centers within the Region (Figure 14), the principal
HSIPT system corridors were designated as shown in Figure
15. This Great Lakes' HSIPT system network joins 17 U.S. SMSAs such as Milwaukee, Chicago, Gary, Indianapolis,
e ^ The NGA Committee on Transportation, Commerce and Technology (1981) , p. 6. 88
GRAND RAPIDS DURAND
p o n t :
JACKSON. DETROIT ANN ARBOl
/ CHICAGO
Figure 11.— Four Possible Intra-State High-Speed Rail Plan.
SOURCE: Michigan Department of Transportation cited from the Lansing State Journal, December 7, 198 0. ■ I ^ ^ B OHIO Service Only J H OHIO Service with Out-of-State Connections
Figure 12.— Ohio Intra-State High-Speed Rail Plan
SOURCE: Ohio Rail Transportation Authority, Ohio High Speed Intercity Rail Passenger Program, p. 39. Compiled by Shun'ichi Hagiwara. Mikneapolis
ston/Neu U) O
Louis Philadelph 14 ’♦Washington Madison 1L Cleveland Milwaukee 12 Akron St. Loul Chicago 13 Youngstown Gary 14 Pittsburgh Southbend 15 Columbu3 a via Louisville Kalamazoo 16 Dayton Grand Rapids 17 Lima B Lansing 18 Cincinnati 9 Detroit 19 Indianapolis 10 Toledo 20 Erie 21 Buffalo
Figure 13.— Preliminary National High-Speed Ground Transportation Network (Part)
SOURCE: U.S. DOT, High Speed Ground Transportation Alternative Study, January 1973, pp. 4-11. non SMSA (U.S.) Census Metropolitan Area (CMA), Canada non CKA (Canada) SMSA Population Cl.000a) 1 Madison 290 2 Milwaukee 1,404 3 Chicago 6,975 A Rockford 272 5 Peoria 342 6 Davenport 363 7 Gary 633 21 Youngstown 537 8 Southbend 280 22 Pittsburgh 2,401 9 Fort Uayne 362 23 Erie 264 10 Indianapolis 1,111 24 Buffalo 1,349 11 Louisville 867 25 Detroit 4 .435 12 Lexington 267 26 Port Huron* 36 to 13 Cincinnati 1,387 © 27 Saginaw 220 H 14 Dayton 853 28 Flint 509 15 Columbus 1,018 29 Ann Arbor 234 16 Lima 210 30 Jackson 259 17 Toledo 763 31 Lansing 424 18 Cleveland 2,064 © 32 Battle Creek 142 19 Akron 679 33 Grand Rapids 539 20 Canton 394 34 Kalamazoo 258 35 Windsor** 259 36 Sarnia*** 79 37 London** 286 38 Toronto** 2,628 39 Hamilton** 499 40 Niagara Falls***303
Figure 14.— Geographical Distribution of SMSAs' Population in the Great Lakes Midwest Region. VD N)
1 Milwaukee 11 Akron 2 Chicago 12 Youngstown 3 Gary 13 Pittsburgh 4 Kalamazoo 14 Colorabus 5 Grand Rapids 15 Dayton 6 Lansing 16 Cincinnati 7 Flint 17 Indianapolis 8 Detroit 18 Uindsor 9 Toledo 19 London 10 Cleveland 20 Toronto
Figure 15.— Proposed Great Lakes Midwest Regional HSIPT System 93
Cincinnati, Dayton, Columbus, Toledo, Cleveland, Akron,
Youngstown, Pittsburgh, Detroit, Flint, Lansing, Grand
Rapids, and Kalamazoo and 3 Canadian CMAs such as Windsor,
London, and Toronto. Missing is a direct route between
Chicago to Toledo, Ohio, via South Bend, which may require
separate explanation-
Rail networks have traditionally been located in
response to political considerations regardless of the
fact that historically highly negative consequences have
resulted from such politically-based decisions. Even now,
the committment or aggressiveness of so-called special
interest groups would be the key for the final decision on a HSIPT system network. Among the so-called Great
Lakes' states of Wisconsin, Illinois, Indiana, Michigan,
Ohio, Pennsylvania, and New York (Minnesota was excluded, because it is not accommodated in the Great Lakes Midwest
Region in this research), Indiana is the only state which has consistently avoided the HSIPT issue. In fact, except
Indiana, such states as Illinois, Michigan, Wisconsin,
Pennsylvania, and New York have been allocating considerable amounts of their budgets to the passenger transportation system study. Where this reluctance of Indiana about passenger transportation system comes from is unclear, but one of the reasons might be that Indiana has, so far, benefited very much from the interstate highway system.
Also, the existence of a single dominant urban canter. 94
Indianapolis, located in the middle of the State and its remoteness from urban centers in other states might have been another reason for Indiana’s indifference to an interstate HSIPT system. The exclusion of Southbend and
Port Wayne from the proposed HSIPT system network could possibly illustrate the problems which those two Indiana urban centers might encounter were they to be excluded from the assumed HSIPT system.
Concepts of “Population Potential" and "Population Energy" as Indices to Measure the Magnitude of the Probable Impacts of a High-Speed Intercity Passenger Train System
A number of the studies concerning the probable impacts of a HSIPT system have been done in Japan. 57
Most of these studies adopted simulation techniques based on an econometric model and could not be free from the
57 Kozo Amano, "The Impacts of the Tokaido Shin Kansen on Regional Economy," Unyu To Keizai (Transportation and Economy), Vol. 29, No. 2. Ittchu Tada, "The Development of the Tohoku District and the Tohoku Shin Kansen," Unyu To Keizai, Vol. 29, No. 2. Miyagi-Ken Tohoku Shin Kansen Kensetsu Suishi Hombu, The Effects of the Construction of the Tohoku Shin Kansen and the Direction of the Regional Improvement. March 1975 are the notable ones among many others. 95 limitations of such techniques as pointed out in the following.
A forecast demand on an econometric model whose parameters are determined from historical relation ships cannot be expected to project truly dramatic traffic shifts, unless a large change in a variable is hypothesized (e.g., the real price of auto gasoline quadruples), or the parameters themselves are arbitrarily adjusted based on the forecaster’s own judgment. The former requires a priori documentation for such a break with historical trend, and the latter removes the rationale for employing models.
For instance, the growth of the GNP assumed by the "flmano
Model"— cumulatively, between 1975 and 1980, 10.7 percent— cannot now be expected and the necessary adjustment based on the actual GNP can be tremendously troublesome. 5 9
In 197 0, Hirozo Ogawa of Hokkaido University tried to clarify the effects of the Tokaido Shin Kansen on regional development by adopting the concept of the
"Demographic Influence" originally developed by
J. Q. Stewart of Princeton University. Ogawa adopted this principle, not only because it was simple and straightforward, but also because it could reflect the
5 8Frank P. Mulvey with National Transportation Policy Study Commission, AMTRAK: AN EXPERIMENT IN RAIL SERVICE, NTPSC Special Report No. 2, September 1978, Wash., D.C.: National Transportation Policy Study Commission, p. 154. 5 9 Concerning the details of the "Amano Model", see Shunichi Hagiwara (1977), pp. 89-91. 96
impact of the reduction of time-distance by the Shin Kansen most rigorously.^
Ogawa applied this "Demographic Influence" concept, which is also called by Stewart the "Population Potential," to the cities on the Tokaido Shin Kansen route where the terminals of the Shin Kansen were located. As mentioned earlier, the "Population Potential" in Stewart’s concept is depicted by the equation, N/d, in which (N) is the population of the city (j) at a distance (d) from the city (i) which can be influenced by the city (j). The equation which illustrates this relationship is:
m X . P. = E G — where P. = population potential at i 1 j=l Dj. Xj = the number of population at j Dij= the distance between i and j G = constant i =1,2, ..... n j =1,2, ,m
Because the objective of Ogawa's research was to investigate the impacts of the time reduction by the Shin
Kansen, he naturally used the time-distance rather than geographical distance between cities. Hence, three different time-distances between the cities on the Tokaido
Shin Kansen route were used. Those are: 1) the time- distance right before the Tokaido Shin Kansen was intro duced; 2) the time-distance at the time the Tokaido Shin
C Q Hirozo Ogawa and Etsuo Yamamura (1975), pp. 27-31. 97
Kansen started its operation in 196 4; and 3) the time- distance after a one-year trial time in 1965. The transition of the time-distance and the results of
Ogawa's study are shown in Table 6.
According to Ogawa, the figures in Table 6 should be interpreted as follows:
1. Because of its own strong population potential, Tokyo
does not receive a significant impact from the drastic
time-distance reduction due to the creation of the
Shin Kansen, but gives a strong impact on others;
2. Yokohama is the only city which experiences a decrease
in its "population potential." This was primarily due
to the poor selection of its terminal. As a matter of
fact, the Shin Kansen terminal for Yokohama was built
in the area far from the downtown Yokohama site which
could have been the optimal site for the terminal.
To make matters worse, the synchronized transportation
systems are not provided for at this new Shin Kansen
terminal. The passengers to and from Yokohama, as a
consequence, rather get on and off the train at the
next terminal, Tokyo, which is just thirty minutes
away by commuter trains;
3. Gifu's potential also does not move up as desired by
the local community. A reason similar to the case of
Yokohama can be cited. The terminal for Gifu City is
actually built at Hajima City, a small city with the TABLE 6.— Transition of Demographic Influence Due to the Shin Kansen.
Tokyo Yokohama Shizuoka Nagoya Gifu Otsu Kyoto Osaka Total
Actual FlRure(Person/minute) March, 1964 252,575 557,840 205,102 240,348 384,879 478,328 415,470 183,990 2,718,532 October, 1964 278,348 521,304 249,243 309,856 401,738 528,785 552,091 253,753 3,095,119 November, 1965 300,120 518,132 311,258 328,B38 432,195 633,406 672,037 317,280 3,567,265
Index (3) March, 1964 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 October, 1964 110.2 93.5 121.5 128.9 104.4 110.6 132.9 137.9 113.9 November, 1965 118.8 92.9 151.7 159.3 112.3 132.4 161.8 172.4 131.2
Source: Htrozo Ogawa, "Kotsu to Toshi Hatten," in Yoahinosuke Yasojima (ed.), Toahl Kotsu Koza I (Lecture Series in Urban Transportation I >. Tokyo: Kashlma Kenkyuaho Shuppankai, 1975. p.29.
Note; The time-distance between Tokyo and Osaka changed as follows:
March 1964 6 hours 00 minutes October 1964 4 hours 00 minutes November 1965 3 hours 10 minutes 99
population of 45,00 0, compared with Gifu's population
of 360,000. An absence of a synchronized transportation
system between these two cities again lessens the
effect of the Hajima terminal;
4. The remaining cities, such as Shizuoka, Nagoya, Otsu,
Kyoto, and Osaka, enjoyed a growth of their population
potentials. Osaka, in particular, increased its
population potential by 7 3 percent.
As a conclusion, Ogawa sees the effects of the
Tokaido Shin Kansen as "the system which joined Osaka and
Kyoto to Tokyo rather than Tokyo to Osaka and Kyoto."
The results of Ogawa's study are well supported by the JNR's data concerning the passengers' on and off at each city's terminal (Table 7). As shown, the number of the passengers who get on and off at the Shin Yokohama terminal, which is the terminal for the city of Yokohama, is the fourth from the lowest with 2,008,901. If one knows that the population of the City of Odawara (whose ridership amount is the fifth from the bottom), is only one-fifteenth of that of Yokohama, it will be quite easy to understand how the selection of the site for the Shin Kansen terminal is important. Also, as expected, Gifu-Hajima's data is the least. This is significant if one knows that the population of Gifu is 2.5 times bigger than that of
Odawara, whose ridership is two times bigger than that of Gifu. Further, the passengers who get on and off at 100
TABLE 7.— Number of Passengers at each Terminal of the Tokaido Shin Kansen (1972).
No. of No. of No. Of Passengers Passengers Passe gets from each Name from each who get on Terminal of Terminal and off at to Terminal to each Shin Osaka (origin) Tokyo Terminal
22,609,686 Tokyo __ 22,609,686 1,744,606 Shin Yokohama 26-',, 295 2,008,901 1,264,166 Odawara 820,279 2,084,445 1,033,328 At ami 1,779,857 2,813,186 978,256 Mlshima 1,854,224 2,832,480 2,332,078 Shizuoka 2,211,329 4,543,407 1,333,684 Hamamatsu 1,572,547 2,906,231 517,908 Toyohashi 1,229,691 1,747,599 5,103,865 Nagoya 7,654,871 12,758,736 691,572 Gifu Hajlma 354,792 1,046,364 542,237 Maibara 1,370,435 1,912,672 149,254 Kyoto 7,042,579 7,191,833 Shin Osaka 11,012,301 11,012,301
38,300,640 37,167,200 75,467,840
Source: Unyu Keizai Kenkyu Senta, Kanaen Kosoku Kotsu Taikel no Seibi Hoahikl nl kanauru Kenkyu Chosa Hokokusho fin Japanese). Tokyo: Unyu Keizai Kenkyu Senta, March 1980. p.23. The original data H ated above were released by the Japanese National Railways(JNR). 101
the Kyoto and Osaka terminals (the combined figure: 18.2 million) is about 8 0 percent of the number for Tokyo.
This figure is extremely high if one knows that the
population of the Osaka metropolitan area (14.2 million
including Kyoto and one other urban prefecture, Hyogo
which is located west to Osaka) is only 57 percent of the
population of the Tokyo metropolitan area, which is composed
of four prefectures with a population of approximately
25.0 million.
Although Ogawa stresses that the study concerning
the effect of the Shin Kansen by adopting the "population
potential" concept can be a simple and reliable way to
reflect some of the important roles which the Shin Kansen
can achieve in regional development process, he himself
admits the shortcomings of his study. The shortcomings
pointed out by Ogawa, himself, are:
1. The study was restricted to only the cities where Shin
Kansen terminals were constructed. The impact of the
Shin Kansen on the adjacent prefectures or cities and
also on the rest of the nation was not figured into
this study;
2. Except for the cases of Yokohama and Gifu (Hajima),
where the terminals were located at extremely
inconvenient places, the internal traffic friction
due to congestion or lack of synchronized transportation
systems in other cities was not taken into account; 102
3. The frequency of the services of the Shin Kansen was
not taken into account.
One of the most important shortcomings of this
study is, however, that the formula for the "population
potential" study cannot take into account the "population
potential" of its own area. Namely, if the time-distance
between a large city and a smaller city was significantly
reduced, its impact on the "population potential" is
always greater to a smaller city and lesser to a large
city. This is often true, but it is also reasonable to
consider that the reduction of the time-distance between
a large city and a smaller city not only stimulates the
smaller city's "population potential," but also the larger
city's "population potential." Later, in 1973, Yamamura
and Maki adopted the aforementioned "population energy"
concept and examined the statistical relationship between
the "population energy" indices of 46 prefectures in Japan 61 and 22 socio-economic variables of those 4 6 prefectures.
The formula used by Yamamura and Maki is as follows. The
"population energy" of each prefecture calculated from the following formula strongly correlated with 22 socio economic variables (Appendix I).
^Etsuo Yamamura and Hiroyuki Maki, "Tohoku Shin Kansen to Dainapolis Keisei (The Tohoku Shin Kansen and the Formation of the Dinapolis)," Chrigaku Kenkyu, Vol. 3, 1973. 103
m X^Xj = Z G ---- where = population energy at i j = l x i = population at i Xj = population at j D^j= the shortest distance or time between i and j b = an exponent describing the effect of the distance or travel time between i and j (distance disutility parameter) G = constant
Yamamura and Maki then utilized the above formula
to predict the possible impacts of the Tohoku Shin Kansen
on the Tohoku region. Although they made a precise
prediction about the growth potentiality of each of six
prefectures in the Tohoku region, the reliability of their
study is not verified yet because of the delay in the
construction of the Tohoku Shin Kansen.
Two studies done by Ogawa and Yamamura and Yamamura
and Maki, respectively, and the study done by Stewart
convinced this author to utilize the two concepts of
"population potential" and "population energy" to project the probable impacts due to the creation of a HSIPT system within the U.S. Region. In the following section, the magnitudes of the probable impacts due to the creation of a HSIPT system on each of the 40 local communities within the Region will be examined. 104
Magnitudes of the Impacts of the Creation of a HSIPT System on the Forty Local Communities Within the Great Lakes Midwest Region
Before getting into the detailed analysis concerning
the magnitudes of the impacts of the creation of a HSIPT
system within the Region, four assumptions, mostly
concerning traveller behavior, were adopted.
1. As shown in Figure 15 on page 92, the proposed HSIPT
system network joins 17 SMSAs and 3 CMAs in Canada;
however, the impact study will be extended to twenty
other local communities in the United States and
Canada. The configuration map of those 4 0 local
communities where the impacts of the creation of the
HSIPT system will be felt is shown in Figure 16;
2. A HSIPT system will definitely be used if the time a
passenger has to be in the train is approximately
4.5 hours. This 4.5 hour ride assumption is based
on the hypothesis about the business trip behavior.
The writer assumes that businessmen travel willingly
if they don't need to leave their homes before 7
o'clock a.m. and are able to come back home by 11
o'clock p.m.
In reality, however, most businessmen will spend a few days for their business trips, as assertained by the
JNR, but it is also true that a considerable number of businessmen will have to spend only one day for their 1 Madison 2 Milwaukee 3 Chicago 4 Rockford 5 Peoria 6 Devenport-Rock Isla -Moline 7 Gary 8 Southbend 9 Fort Wayne 10 Indianapol 11 Louisville 21 Youngstown 22 Pittsburgh
12 Lexington 105 13 Cincinnati 23 Erie 14 Dayton 24 Buffalo 15 Colombus 25 Detroit 16 Lima 26 Port Huron 17 Toledo 27 Saginaw 18 Cleveland 28 Flint 19 Akron 29 Ann Arbor 20 Canton 30 Jackson 31 Lansing 32 Battle Creek The Planned High-Speed Intercity Passenger 33 Grand Rapids Train Network 34 Kalamazoo The Interstate Highway Network 35 Windsor 36 Sarnia 37 London 38 Toronto 39 Hamilton 40 Niagara Falls
Figure 16.— The Planned High-Speed Rail Network and 40 Local Communities. 106
business trips. (According to the JNR's data explained
in the previous chapter, almost 12 percent of the Shin
Kansen passengers spent a single day for their trips.)
Although the writer assumes that businessmen in the
United States will stay as much as four and a half hours
in the train, if it is necessary, 90 percent of the
Japanese Shin Kansen users have spent about 3.5 hours in
the train. During this 3.5 hour ride in the Shin Kansen, the Japanese businessman can travel approximately 350 miles
at an average speed of 100 mph. The American businessmen, on the other hand, can travel approximately 5 00 miles with an assumed average speed of 110 mph.
A typical businessman's single day trip schedule is likely to be as follows: For a Chicago businessman who wants to travel to Cleveland, o o « *» ------Leave home by car 0.5 hours 7: 3 0 : Take the first train at Chicago terminal 4.5 12:00: Get off the train at Cleveland terminal 1.0 1: 00- Business deal or meeting starts 4.0 5: 00: Business deal or meeting adjourns 1.1 6: 101 Take the train from Pittsburgh at 4.5 Cleveland terminal 10:40n Get off the train at Chicago terminal 0.5 11:10^ Back home 107
For a Lansinq businessman who wants to travel to Cleveland 00 in o ■ Leave home by car 0.5 hours 9.20: Take the first train from Chicago at 2.5 " Lansing terminal 11:50: Get off the train at Cleveland terminal 1.1 1:00: Business deal or meeting starts 4.0 5:00: Business deal or meeting adjourns 1.1 6:10: Take the train from Pittsburgh at 2.5 Cleveland terminal 8:40: Get off the train at Lansing terminal 0.5 9:10- Back home
Another possible case would be that of a business man who lives in a community where a HSIPT system terminal will not exist:
For a Battle Creek businessman who wants to travel to Cleveland,
8: 00 - Leave home by car 1.3 hours 9:20: Take the first train from Chicago at 2.5 II Lansing terminal 11:50- Get off the train at Cleveland terminal 1.1 II 1:00: Business deal or meeting starts 4.0 II 5:00 = Business deal or meeting adjourns 1.1 rt 6:10 : Take the train from Pittsburgh at 2.5 n Cleveland terminal 8:40: Get off the train at Lansing terminal 1.3 it 10:00- Back home
According to the tourbook published by the American
Automobile Association (AAA), the driving time between
Battle Creek and Cleveland is assumed to be approximately
6 hours. It-implies that, in order to be at Cleveland 108 sometime around 12 o'clock, the businessman at Battle Creek has to leave his home by 6 o'clock in the morning. It is an unrealistic assumption that this "typical" businessman will be willing to leave home this early.
The above assumption for the Battle Creek business man, however, is assumed to be unworkable for the business man at Cleveland who wants to travel to Battle Creek.
The trip schedule for the Cleveland businessman to Battle
Creek would be as follows:
7:40* Leave home by car 0.5 hours 8:10 = Take the first train from Pittsburgh at 2.5 11 Cleveland terminal 10:40: Get off the train at Lansing terminal 0.3 11 11:00: Take the rent-a-car to Battle Creek 1.5 II 12:30= Arrive at Battle Creek 0.5 11 1:00* Business deal or meeting starts 4 . 0 If 5:00 = Business deal or meeting adjourns 1.5 It 6:30: Arrive at Lansing 0.8 11 7:20 = Take the train from Chicago at Lansing 2.5 If terminal 10:10: Get off the train at Cleveland terminal 0.5 II 10:40- Back home
The total travel time necessary for the Cleveland businessman's trip to Battle Creek is only one hour longer than that of the Battle Creek's businessman to Cleveland; however, the necessary time for driving a car for the
Cleveland businessman, which is four hours, is 1.4 hours 109
(54 percent) longer than that of the Battle Creek businessman.
The writer simply negates this sort of assumption.
The predictable, and also unpredictable, time-loss in the above travel schedule for the Cleveland businessman should be assumed to reduce the travel opportunity to Battle
Creek or the businessman concerned should be assumed to take his own car or airplane to get to Battle Creek.
3. A HSIPT system will not be utilized if the necessary
travel distance to the destination is less than 100
miles. Even in Japan, only 30 percent of the Shin
Kansen users are utilizing the system for such a
short distance. It will be quite reasonable to make
an assumption such as this in such a nation as the
United States where the highway network systems are
extensively developed;
4. A HSIPT system will not be utilized if the necessary
travel distance to the destination is more than 60 0
miles or so. This sort of long distance should be,
and most likely would be, covered by the air mode in
terms of today's governing factors.
On these bases, the necessary time-distance matrices between 40 origins and 20 destinations were produced (Appendices Ila and lib). 110
Changes of the "Population Potential11 of the 4 0 Local Communities Within the Region Before and After the Creation of a HSIPT System
Two "Population Potentials" were calculated for
each of the 4 0 local communities within the Region. One
is based on the time-distance by automobile (before) and
the other is based on the time-distance by a HSIPT system
(after) .
The formula used for this part of the research is
similar to the one used by Ogawa, except the use of
parameter b. That is:
m Xj P. = I ------where = population potential at i j=l Xj = population at j -1 Dii= t^ie shortest distance or time between i and j b = an exponent describing the effect of the distance or travel time between i and j (distance decay parameter) l "■ 1/ 2 / >■«■•! n j =1,2, .... . m
Although the shortcoming of this formula is undeniable, it is still worthwhile to examine the popula tion potential of the local communities by this formula.
By calculating the population potential using the above formula, the level of influence given by other communities due to the reduction of the time-distance by the creation of a HSIPT system within the Region will be examined.
The results of the calculation are shown in
Appendices III and IV. As shown in Appendix III, the Ill
correlation between the "population potential" and the
population of each community is very low. The highest
correlation coefficient obtained is only 0.413, at b = 1.0.
The second highest correlation coefficient is 0.35 8, at b = 0.5. The third highest and the smallest are 0.333, at b = 1.5 and 0.134, at b = 2.0, respectively. From these values, it becomes clear that, although the correla tion is not so high, the "population potential" of each local community is less restricted by time-distance. The reason for this interpretation comes from the nature of the distance disutility parameter, or distance decay parameter, (b). The values of (b) have to be interpreted as "the smaller the value of (b), the distance or time is less restrictive." For instance, if the value of the exponent b for school trips is 2.0 and for shopping trips,
1.0, this should be interpreted as "people are willing to travel farther for shopping than they are for school."
The adoption of the exponent b is, as such, important to reflect a behavioral aspect in the formulations.
The most highly correlated "population potentials" are grouped in Table 8, based on their strength. Table 9 shows the "population potentials" based on the time- distance by a HSIPT system at b = 1.0. The rate of changes of the "population potentials" for 40 communities are summarized in Table 10. TABLE 8.— Population Potential at Each SMSA Based on the Time- Distance by Automobile at b = 1-0. Unit: Person/Hour
Population Population Population Population SMSA Potential SMSA Potential SMSA Potential SMSA Potential
Peoria 4,999.4 Canton 9,193.2 Gary 12,579.0 Uindsar 28,462.5 Louisville 4,980.6 Battle Creek 9,126.2 Ann Arbor 12,456.7 Chicago 18,641.2 Lexington 4,978,4 Southbend 9,060.2 Toledo 11,490.1 Detroit 16,165.1 Davenport 4,671.1 Sarnia 9,008.2 Cleveland 11,062.9 Port Huron 8,953.1 Flint 10,173.7 Toronto 8,932.1 Akron 10,083.0 Kalamazoo 8,836.0 Pittaburgh 9,616.3 Youngstown 8,829.9 Jackson 9,538.0 Columbus 8,705.0 Milwaukee 9,448.7 Cincinnati 8,605.3 Lansing 9,362.2 Dayton 8,506.6 Indianapolis 8,300.6 Lima 8,232.4 Grand Rapids 8,023.0 Fort Wayne 7,914.2 Saginaw 7,622.9 Rockford 7,362.8 Hamilton 7,181.3 London 6,772.2 Erie 5,897.6 Kean * 9,257.8 Standard Deviation (sd.) - Madison 5,720.2 Niagara Falls 5,577.5 Buffalo 5,243.4 5,054.2 (1 Bd.) 9,257.8 (1 ed.) 13.461.4
Note: The "population potentials" listed above ere calculated aa follows:
where Pt « population potential at 1 X) ** population at j DjLj ** the shortest time-distance between 1 and j (see Appendix Ila) i M 1, 2, ...... 40 . . . i j » 1, ...... , 2 0 (the number of communities where the terminal Is planned; TABLE 9.— Population Potential at Each SMSA Based on the Time- Distance by the HSIPT System at b = 1.0. Unit: Person/Hour
Population Population Population Population SMSA Potential SMSA Potential SMSA Potential SMSA Potential
Lexington 6,656.9 Milwaukee 11,484.2 Toledo 15,477.3 Windsor 31,577.5 Erie 6,647.2 Port Huron 11,293.2 Cary 15,212.2 Chicago 20,938.8 Madison 6,368.0 Sarnia 11,287.3 Ann Arbor 15,146.0 Detroit 20,570.8 Niagara Falla 5,771.5 Toronto 10,810.1 Cleveland 15,112.9 Louisville 5,672.2 Youngstown 10,571.4 Flint 14.355.3 Buffalo 5,265.2 Saginaw 10,410.4 Grand Rapids 13,861.9 Peoria 5,049.2 Canton 10,386.8 Kalamazoo 13,729.2 Davenport 4,647.7 London 10,172.5 Akron 13.226.9 Southbend 9,653.0 Columbua 13.163.7 Lima 9.043.5 Lansing 13,149.4 Fort Wayne 8.194.5 Cincinnati 12.831.1 Rockford 7.794.5 Indianapolis 12.505.9 Hamilton 7,534.7 Dayton 12.045.1 Jackson 12.035.2 Pittsburgh 11.90B.9 Battle Creek 11,606.6
6,536.2 (1 sd.) 11,569.2 (1 sd.) 16,602.2
Mean “ 11,569. 2 Standard Deviation (sd.) - 5,033.0
Mote: See note In Table 8. Appendix lib should be referred for the values of D ^ . TABLE 10.— Rates of Change of the Population Potential after the Creation of the HSIPT System.
SMSA Rate SMSA Rate SMSA Rate SMSA ‘Rate
Southbend 1.065 Pittsburgh 1.238 Dayton 1.416 Grand Rapids 1.728 Rockford 1.059 Ann Arbor 1.216 Flint 1.411 Kalamazoo 1.554 Hamilton 1.049 Milwaukee 1.215 Saginaw 1.367 Columbus 1.512 Fort Wayne 1.035 Toronto 1.210 Cleveland 1.366 Indianapolis 1.507 Niagara Falls 1.035 Oary 1.209 Toledo 1.347 London 1.502 Peoria 1.010 Youngstown 1.197 Akron 1.312 Cincinnati 1.491 Buffalo 1.004 Louisville 1.139 Lexington 1.297 Lansing 1.441 Davenport 0.995 Canton 1.130 Detroit 1.273 Chicago 1.123 Battle Creek 1.272 Madison 1.113 Jackson 1.262 Windsor 1.109 Port Huron 1.261 Lima 1.099 Sarnia 1.253 Erie 1.093
1.070 (1 sd.) 1.248 (1 sd.) 1.426
Mean - 1.248 Standard Deviation (sd.) “ 0.178
Mote: The rates of change listed above are calculated as follows:
Rate of Change ** by automobile In Table 8/ Pj by the HSIPT system In Table <7. 115
The data shown in Tables 8, 9, and 10 should be
interpreted as follows:
1. Among three communities/ Windsor, Chicago, and Detroit,
which are in the highest bracket in terms of "population
potential" for both before and after the creation of a
HSIPT system, Detroit is likely to receive the
strongest effect w from the reduction of the time- distance by the system. In fact, the impact on
Windsor and Chicago is quite small;
2. Among the other 37 local communities, Grand Rapids,
Kalamazoo, Columbus, Indianapolis, London, Cincinnati,
and Lansing will be influenced by the new system most
strongly. Eleven other communities, such as Dayton,
Flint, Saginaw, Cleveland, Toledo, Akron, Lexington,
Battle Creek, Jackson, Port Huron, and Sarnia, will be
influenced by the system more than average. Among the
communities where direct service by a HSIPT system
terminal is not planned, Saginaw will be most
influenced by the system. Davenport's population
potential, on the other hand, grew less after the
creation of the new system.
The nature of the influence of a HSIPT system is unpredictable. If the application of the analogy from the
Japanese experience is allowed, those communities such as
Grand Rapids, Kalamazoo, Columbus, Indianapolis, London,
Cincinnati, and Lansing might enjoy a large number of 116
"on" and "off" passengers at their terminals. This,
however, does not imply the same positive impacts to
those communities concerned as seen in some of the
Japanese communities along the Shin Kansen routes.
Suffice it to say that careful planning and study can
alleviate negative influences from such unpredictableness.
The necessary actions for this will be discussed in the
following chapters.
As a next step, to alleviate the shortcomings of
the "population potential" concept, the "population energy" concept, which at least can take into account its own
"population potential," was calculated. The results of the calculation are listed in Appendices V and VI. As shown in Appendix V, the correlation between "population energy" and the population of each community is fairly high, except in the case of b = 2.0 (0.503). The highest correlation coefficient was 0.989, at b = 0.5. Tables 11,
12, and 13 are the summarization of "population energy" for both before and after the creation of a HSIPT system in the Region. The data shown in those tables should be interpreted as follows:
1. As shown, large cities such as Chicago and Detroit
have an extremely high "population energy," a
reasonable finding. Cleveland, Pittsburgh, and
Toronto, all of these three SMSAs having a population
of more than 2 million, also compose the highest TABLE 11.— Population Energy (Energy of Interchange) at each SMSA Based on the Time-Distance by Automobile at b = 0.5. Unit: Square-Person/Hour
Energy of Energy of Energy of Energy of SMSA Interchange SMSA Interchange SMSA Interchange SMSA Interchange
Gary 10,398,080.5 Milwaukee 17,755,178.4 Chicago 67,637,409.9 Louisville 10,168,026.8 Cincinnati 17,073,309.3 Detroit 54,695,874.6 Akron 9,922,067.2 Buffalo 16,059,157.2 Cleveland 27,086,132.1 Flint 7,770,950.2 Indianapolis 14,211,255.3 Pittsburgh 26,319,022.7 Youngstown 7,465,661.7 Columbus 13,533,359.0 Toronto 25,569,468.8 Grand Rapids 7,408,404.8 Toledo 12,290,285.1 Hamilton 6,583,315.8 Dayton 11,310,543.2 Lansing 6,378,655.4 Canton 5,928,170.5 Windsor 5,566,960.3 Fort Wayne 5,426,755.1 Note: Population Energies listed above are Southbend 4,369,727.3 calculated as follows: 117 Ann Arbor 4,100,881.0 Davenport 4,098,968.0 Jackson 4,082,186.1 *■ si X jX j Peoria 3,993,794.9 El “ 1 ' 'b Kalamazoo 3,8B7,776.4 J-l Dij Rockford 3,712,824.6 London 3,697,853.7 where Ei - population energy at 1 Niagara Falls 3,669,816.9 }q - population at 1 Madison 3,537,954.9 Xj - population at J Erie 3,334,222.3 Dji - the shortest tlma--distance Lima 3,184,058.9 between 1 and j (see Appendix Ha) Saginaw 3,125,985.2 b - 0.5 Lexington 3,105,752.3 1 - 1, 2...... ,40 Battle Creek 2,239,899.9 j “ 1, 2, 20 Sarnia 1,201,016.3 Port Huron 544,583.5
- 2,472,817.1 (1 ad.) 11,060,903.9 (1 sd.) 24,594,784.9
Mean - 12,060,983.9 Standard Deviation (sd.) -13,363,557.7 TABLE 12.--Population Energy (Energy of Interchange) at each SMSA Based on the Time-Distance by the HSIPT System at b = 0.5. Unit: Square-Person/Hour Energy of Energy of Energy of Energy of SHSA Interchange SMSA Interchange SMSA Interchange SMSA Interchange
Gary 11,878,748.0 Toronto 30,313,974.7 Chicago 82,B82,288.5 Akron 11,640,044.9 Cincinnati 22,534,584.6 Detroit 72,499,582.7 Louisville 10,835,329.6 Milwaukee 20,657,337.3 Cleveland 34.318.007.0 Grand Rapids 10,123,928.3 Indianapolis 18,127,095.9 Pittsburgh 31.212.224.0 Flint '9,733,580.5 Columbus 17,687,545.2 Youngstown 8,333,870.1 Buffalo 16,092,5D8.1 Lansing 7,877,121.3 Toledo 14.79B.779.5 Hamilton 6,757,707.6 Dayton 14,249,929.6 Canton 6,355,912.2 Windsor 6,336,323.4 Fort Wayne 5,529,07B.0 Kalamazoo 4,879,902.0 Jackson 4,706,099.8 Ann Arbor 4,700,002.3 Southbend 4,524,736.8 London 4,518,069.0 Davenport 4,090,235.8 Feoria 4,011,809.0 Rockford 3,842,294.2 Saginaw 3,761,444.0 Niagara Falls 3,733,657.5 Madison 3,708,646.9 Lexington 3,546,304.3 Erie 3,486,381.7 Lima 3,335,655.7 Battle Creek 2,524,004.6 Sarnia 1,385,521.0 Port Huron 630,522.0
- 3,864,743.8 U sd.) 13,304,144.6 (1 sd.) 30,473,033.0
Mean 13,304,144.6 Standard Deviation (sd.) 17,168,888.4
Note: See note in Table 11. Appendix lib Bhould be referred for the values of D^j. TABLE 13.— Rate of Increase of Population Energy (Energy of Interchange) after the Creation of the HSIPT System.
SMSA Rate SMSA Rate SMSA Rate SHSA Rate
Madison 1.048 Ann Arbor 1.146 Flint 1.253 Grand Rapids 1.364 Lima 1.048 Gary 1.142 Lansing 1.235 Detroit 1.326 Erie 1.046 Lexington 1.142 Chicago 1.225 Cincinnati 1.320 Southbend 1.035 Windsor 1.138 London 1.222 Columbus 1.307 Rockford 1.035 Battle Creek 1.130 Toledo 1.204 Indianapolis 1.276 Hamilton 1.026 Youngstown 1.116 Saginaw 1.203 Cleveland 1.267 Fort Wayne 1.019 Canton 1.072 Pittsburgh 1.186 Dayton 1.260 Niagara Falla 1.017 Louisville 1.066 Toronto 1.185 1 Kalamazoo 1.255 Peoria 1.005 Akron 1.173 Buffalo 1.002 Milwaukee 1.163 Davenport 0.998 Port Huron 1.158 Sarnia 1.154 Jackson 1.153
1.051 (1 ad.) 1.153 (1 ad.) 1.255
Mean 1.153 Standard Deviation (sd,)- 0.102
Note: The rates of change listed above are calculated as follows:
Hate of change - El by automobile in Table LI/ El by the HSIPT system In Table 12. 12 0
bracket. Among these large SMSAs, however, Detroit
is likely to have the strongest impact from a HSIPT
system and Cleveland follows Detroit in impact reception;
2. Among the middle sized SMSAs, such as Cincinnati,
Milwaukee, Indianapolis, Columbus, Buffalo, Toledo
and Dayton (all of these having the population size
of 800,000 to 1.5 million), Cincinnati is likely to
have the strongest impact from the system. Columbus,
Indianapolis, and Dayton, then, follow Cincinnati.
Buffalo, on the other hand, will be excluded from the
effects of the system completely;
3. Among the SMSAs in the third bracket, Grand Rapids is
likely to receive the strongest impacts from the
system. Kalamazoo, Flint, Lansing, London, and Akron
will follow Grand Rapids. Among the SMSAs where a
HSIPT system terminal is not planned, Saginaw is
likely to have the strongest impacts from the system.
Port Huron, Sarnia, and Jackson will follow Saginaw.
From the two studies described above, it can be concluded that the new system will influence most signif icantly Grand Rapids, Michigan; Columbus, Ohio; Cincinnati,
Ohio; Kalamazoo, Michigan; and Indianapolis, Indiana.
Then, Detroit, Michigan; Cleveland, Ohio; Lansing, Michigan; and London, Ontario, Canada will follow the above five
SMSAs. The other nine communities, such as Flint, 121
Michigan; Toledo, Ohio; Saginaw, Michigan; Battle Creek,
Michigan; Jackson, Michigan; Port Huron, Michigan; Sarnia,
Ontario, Canada; and Lexington, Kentucky, are also likely to have strong impacts from the new system.
As indicated, the communities in Michigan are
likely to experience the most significant impacts from the creation of a HSIPT system within the Region. The result, however, would have been different if a different network were planned. Canton, Ohio, for instance, which has a strong locational advantage to such large SMSAs as
Cleveland and Pittsburgh, is left out from the influence of the new system. South Bend and Fort Wayne, both in
Indiana, are also left out from the influence of the new system completely. It will be reasonable to assume that the influence of a HSIPT system will be considerably different if a new network is planned to join them.
It is natural to estimate that the introduction of a sharp reduction of the time-distance between SMSAs due to the creation of a HSIPT system will result in significant impacts on those SMSAs. The problem will be how to deal with the strong impacts introduced by the new system. Careful analyses of the impacts and development of a comprehensive plan to alleviate the possible negative impacts will be essential. In the next chapter, the probable impacts on local communities due to the creation of the HSIPT system in the Region will be specifically 122
investigated. In fact, as experienced in Japan, the impacts from such a new transportation system are enormous.
The impacts on land uses and covers, economic and social structures, other modes of transportation such as the highway and air, and environmental conditions have to be projected. Most importantly, the involvement of political considerations concerning the selection of the site for a HSIPT system terminal have to be carefully watched. CHAPTER IV
PROBABLE IMPACTS ON A LOCAL COMMUNITY DUE TO THE CREATION OF A HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM WITHIN THE GREAT LAKES MIDWEST REGION (CASE STUDY: THE LANSING METROPOLITAN AREA IN MICHIGAN)
Probable Impacts on Rural-Urban Structures (Impacts on Land Uses and Land Covers and Population Settlement Patterns)
Transportation and land use have a symbiotic
relationship with each other. This strong relationship
should be seen from two different aspects: 1) transporta
tion as a consumer of scarce urban land and 2) trans
portation as a provider of access to non-transportation
land-consuming activities. 62 In fact, the necessary space
for highway systems and airports often requires enormous
amounts of land in the middle of or in the vicinity of
urban areas, and they have become serious threats to preservation efforts for productive agricultural lands,
forest lands, and flood plains in local communities.
A HSIPT system, on the other hand, requires much less space compared to the above two transportation modes.
62 D. B. Lee, Jr. and C. P. Averous developed this distinction in their paper titled "Land Use and Transporta tion: Basic Theory," Environment and Planning, Vol. 5, 1973, pp. 491-502.
123 124
In fact, the necessary width of a two-track HSIPT system would be as wide as 10 to 11 m compared to that for four- g 2 lane highway system which can be more than 25 m. Even the necessary space for a HSIPT system terminal is small.
For instance, the ordinary HSIPT system terminal in Japan has two or three platforms of 500 m or so; accordingly, it is reasonable to assume that all of the necessary facilities related to a HSIPT system terminal could be accommodated in the area of one square mile.
This, however, does not mean to negate the necessity of cautious planning for the location of a HSIPT system^ right-of-way and terminal. As a matter of fact, negative ramifications from careless planning could be tremendous.
Such negative effects from a HSIPT system as noise and vibration will degrade the quality of land along the route and will result in a sharp decline of the value of land.
A carelessly planned HSIPT system terminal could result in the waste of valuable land, as occasionally seen in Japan.
In fact, the lack of follow-up development in and around carelessly planned sites has often been seen in Japan.
To minimize the negative effects on land uses and land covers in the local areas, the following considerations are prerequisites:
6 3 The figures are based on the Japanese Standards. 125
1. A HSXPT system right-of-way should not be placed on
productive agricultural lands, on forest lands, or
within flood plains;
2. A HSIPT system right-of-way should be located so as
to be away from densely populated areas;
3. A HSIPT system terminal should be located so as to be
functionally linked with the existing commercial center
of the area concerned, such as the central business
district or commercial core. This linkage is necessary
to avoid drastic land use changes in the local areas
and to avoid inefficient and non-effective dual
investments in small local areas;
4. A HSIPT system terminal should be located so as to
synchronize effectively with existing transportation
systems such as highways, buses, mass transits, and
air terminals. This is necessary to avoid unnecessary
investments of valuable urban land to provide the
necessary transportation system with a planned or
developed HSIPT system terminal.
By taking into account the above four prerequisites, the HSIPT system planners can alleviate negative impacts due to the creation of a HSIPT system on land uses and land covers. For instance, if one observes the maps of five different land uses and covers in the Lansing Metro politan Area which are shown in Figures 17, 18, 19, 20, and 21, it will be relatively easy to reach the optimal 12 6
1 Lebanon 25 Kalamo 2 Essex 26 Carmel 3 Creenbush 27 Eaton 4 Duplaln 28 Eaton Rapids 5 Dallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield 8 Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley 34 Meridian 11 Olive 35 Wllliamston 12 Victor 36 Locke 13 Eagle 37 Delhi 14 Watertown 38 Alaiedon 15 Dewitt . 39 Wheatfield 16 Bath 40 Leroy 17 Sunfleld 41 Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whiteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 78 47 Bunkerhill 24 Windsor 48 Stockbrldge
3 jl £ fwrrcrasn
1-96
g US-127 US-27 Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48)
Figure 17.— Location of Nine Major Urban Centers in the Lansing Metropolitan Area. 127
Lebanon 25 Kalamo Essex 26 Carmel Greenbush 27 Eaton Duplaln 28 Eaton Rapids Dallas 29 Bellevue Bengal 30 Walton Bingham 31 Brookfield B Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley 34 Meridian 11 Olive 35 Williamston 12 Victor 36 Locke 13 Eagle 37 Delhi 14 Watertown 38 Alaiedon 15 Dewitt 39 Wheatfie Id Bath 40 Leroy 17 Sunfleld 41 Aurelius IS Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whiteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhill Windsor 48 Stockbrldge
Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48)
Figure 18.— Location of Major Agricultural Lands in the Lansing Metropolitan Area.
SOURCE: Tri-County Regional Planning Commission. Michigan Tri-County Region Environmental Framework Study. Date Unknown, p. 33. 128
1 Lebanon 25 Kalamo 2 Essex 26 Carmel 3 Greenbush 27 Eaton 4 Duplaln 28 Eaton Rapids 5 Dallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield 8 Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley 34 Meridian 11 Olive 35 Williamston 12 Victor Locke 13 Eagle 37 Delhi 14 Watertown 38 Alaiedon 15 Dewitt 39 Wheacfield 16 Bath 40 Leroy 17 Sunfleld Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta Whiteoak Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhill 24 Windsor 48 Stockbrldge
Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48)
Figure 19.— Location of Major Woodlands in the Lansing Metropolitan Area.
SOURCE: Tri-County Regional Planning Commission. Michigan Tri-County Region Open Spaces of the Region: Principles of Open Space Planning, Dec. 1969, p. 24. 129
Lebanon 25 Kalamo Essex 26 Carmel Greenbush 27 Eaton Duplaln 28 Eaton Rapids Dallas 29 Bellevue Bengal 30 Walton Bingham 31 Brookfield 8 Ovid Hamlin 9 Westphalia 33 Lansing 10 Riley 34 Meridian Olive 35 Williamston 12 Victor Locke 13 Eagle Delhi 14 Watertown 38 Alaiedon Dewitt 39 Wheatfield 16 Bath 40 Leroy 17 Sunfleld Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whiteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhill 24 Windsor 48 Stockbridge
k
Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48)
Figure 20.— Location of Major Flood Plains in the Lansing Metropolitan Area. SOURCE: Tri-County Regional Planning Commission. Michigan Tri-Countv Region Environmental Framework Study. Date Unknown, p. 27. 130
1 Lebanon 25 Kalamo 2 Essex 26 Carmel 3 Greenbush 27 Eaton A Duplaln 28 Eaton Rapids 5 Dallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield 6 Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Rile/ 34 Meridian 11 Olive 35 Williams tan 12 Victor 36 Locke 13 Eagle 37 Delhi 11 Watertown 38 Alaiedon 15 Dewitt 39 Wheatfield 16 Bath 40 Leroy 17 Sunfleld 41 Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whiteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhill 24 Windsor 48 Stockbrldge I i f i
I
Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) E Ingham County (33-48)
Figure 21.— Location of Major Groundwater either Sensitive or Developable in the Lansing Metropolitan Area. SOURCE: Tri-County Regional Planning Commission. Michigan Tri-Countv Region Environmental Framework Study. Date Unknown, p. 23. 131
(or quasi-optimal) locations of a HSIPT system route and terminals. The area suitable for such a terminal should be designated in one of seven township areas: Watertown,
Dewitt, Bath, Delta, Windsor, Delhi, and Alaiedon. Lansing and Meridian Townships have to be excluded from the site proposed for a HSIPT system terminal because these two townships are substantially developed and populated. By the same token, a HSIPT system route should be planned to run through two possible corridors; one is within such townships as Eagle, Watertown, Dewitt, and Bath, and the other is within Oneida, Windsor, Delhi, Alaiedon, Wheat- field, and Locke (Figure 22).
As far as a HSIPT system is concerned, however, the most important role is that of a provider of access to non-transportation, land-consuming activities. In fact, a HSIPT system not only opens the specific local areas to the rest of the region or the nation, but also gives a significantly improved accessibility to certain portions of the local areas concerned. Improved accessibility will eventually give rise to rural-urban economic, social, and physical structural changes in the areas concerned. If some portion of a local area receives a significant locational advantage from the creation of a HSIPT system terminal, the developmental potential of that portion concerned will increase considerably, especially the developmental potential of residential types of use 132
1 Lebanon 25 Kalamo 2 E s sex' 26 Carmel US-27 2 Greenbush 27 Eaton 4 Duplaln 28 Eaton Rapids 5 Pallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield 8 Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley o ires 34 Meridian 11 Olive 35 Willianston 12 Victor 36 Locke 13 Eagle 37 Delhi 14 Watertown 38 Alaiedon 15 Dewitt 39 Wheatfield 18 Bath 40 Leroy 17 Sunfleld 41 Aurelius 18 Roxand 42 Vevay 19 O neida 43 Ingham 20 Delta 44 Uhlteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhlll 24 Windsor Grand f 48 Stockbrldge Rapids 1 1 infa ■
Flint
O LC
P US-127 US-2 7
Possible HSIPT System Terminal ••••••••••; Possible HSIPT System Route
Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17—32) Ingham County (33-48)
Figure 22.— Possible Locations of the Lansing HSIPT Terminal and HSIPT System Route. 133
activity. As a matter of fact, accessibility and residen
tial land use have had a strong interrelationship. For
instance, Hansen says that "an empirical examination of the
residential development pattern illustrates that acces
sibility and the availability of vacant developable land
can be used as the basis of a residential land use model."
In the text that follows, the concept of accessibility,
which is necessary for an understanding of the role of
transportation as a provider of access to non-transportation,
land-consuming activities, will be examined and applied to
analyze the probable impacts of a HSIPT system on land uses
and covers in the Lansing Metropolitan Area.
Concept of Accessibility to Analyze the Relationship between Transportation and Land Use
The concept of accessibility has been used
frequently in both transportation and planning studies.
This concept, however, is not easy to define in clear and
quantifiable terms. On the one hand, accessibility could
be conceptualized as a measure of spatial opportunities which are inherent at any location. On the other hand, accessibility has been primarily synonymous with the minimization of the costs of friction as perceived in the
64 Walter G. Hansen, "How Accessibility Shapes Land Use," Journal of American Institute of Planners, Vol. 25, 1959, pp. 72-76. literature of economics. This concept of accessibility has been developed in the discipline of location theory for both industrial locations (Weber, 1929; Losch, 1954; and Isard, 1956) and residential locations (Wingo, 1961;
Alonso, 1964). Isard has noted, in conventional economic theory, that "transport costs and other costs involved in movement within a 'market' are assumed to be zero. In this sense the factor of space is repudiated, everything within the economy is in effect compressed to a point, and 6 5 all spatial resistance disappears." In these two fields, the fields of residential location theory and industrial location theory, analysis has concentrated on the trade-off between transport costs and rents. Further, in these analyses, accessibility has been considered in unidimen sional terms as minimizing distance from a city center; in other words, the Central Business District (CBD).
Recently, however, people have been more interested in such matters as low tax rates, the high quality of schools, availability of recreational spaces and facilities, crime- less or free environments, and high environmental quality.
This sort of human behavior cannot be analyzed in the traditional model of residential location theory in which the distance from the CBD was assumed to be the single key element to figure out accessibility. According to this
65 Walter Isard, Location and Space Economy, Cambridge: The MIT Press, 195 6, p. 26. 135 assumption, the CBD and its vicinity where job opportunity is assumed have always had highest accessibility, and the distance from the CBD is always assumed to be a disutility.
In reality, however, many affluent people choose to live in areas of low accessibility and other people, whose ability to receive the opportunities inherent in their spatial location may be very low, live in areas of rel atively high accessibility. Alonso, as previously noted, introduced the size of the site element into the location decision by people and stressed that the trade-off relationship between accessibility and space is the main concern for people who want to make a choice among alternative locations. This concept of the availability of developable, vacant land is certainly the key element to understanding the location decisions of people and entrepreneurs. Without taking into account this element, such a series of phenomena as suburbanization, exurbaniza tion, and gentrification movements, which have been noticeable in many of the urban areas in the United States, cannot be understood.
The above-mentioned traditional trade-off theory has been in the main stream of residential location theory; however, its underlying assumptions, such as a single employment center in the CBD, no locational externalities, transport cost savings as the sole determinant of location rent, the negligibleness of density function, environmental 136 quality, etc. , make the trade-off model a very special case which is of little help to understanding the residen tial location behavior of people in a modern city.
Although the traditional trade-off models have been remarkably resilient, a number of the alternatives have 6 G been proposed to alleviate its unidimensional concept.
Many of those alternatives have emphasized the importance of environmental externalities in the residential location choice (Ellis, 1967; Yamada, 1972; Richardson, 1977); others have emphasized that accessibility could no longer be figured out by minimizing distance from the CBD (Stegman,
197 4; Senior and Wilson, 197 4; Beckman, 1973; Richardson,
1977). The complexity of those alternatives, however, have not succeeded in replacing the traditional trade-off models, which in some sense has a remarkable simplicity.
R. Vickerman tried to clarify various concepts of accessibility. He examined a geographical concept of accessibility which, according to him, involves a combination of two elements: location on a surface relative to suitable destinations and the characteristics of the transport network or networks linking points on
W. Richardson concisely summarizes the state of affairs of the alternatives to the traditional standard trade-off models. See, H. W. Richardson, "A Generalization of Residential Location Theory," Regional Science and Urban Economics, 7, 1977, pp. 251-266. 137
67 that surface. The former element was originated by
Christaller's central place theory (Christaller, 1966) and the latter have been developed by Shimbel (1953),
Kansky (1963), Haggett and Chorey (1969), and Hay (1973).
He also examined the economic concept of accessibility originated by Weber (1929) , Losch (1959), Isard (1959) ,
Wingo (1961) , and Alonso (1964) and further, the concept of accessibility based on attraction and potential which was primarily discussed and introduced by Harris (1954),
Hansen (1959), and later by Wilson (1971). Among these, according to Vickerman, the attraction-accessibility concept associated with the spatial interaction model is the most satisfactory, although there exists a high degree of intercorrelation among the variables usually selected to identify the effects of attraction and accessibility.
This measure is also superior to a similar measure, the
Shimbel Index, which does not accommodate "a behavioral aspect into the formulations which is plausible on a priori grounds— that the perception of accessibility declines 6 8 increasingly rapidly as distance increases." Turning to the issue of intercorrelation which was pointed out by Vickerman, factor analysis has been shown
67 R. W. Vickerman, "Accessibility, Attraction, and Potential: A Review of some Concepts and their Use in Determining Mobility," Environment and Planning A , Vol. 6, 1974, pp. 675-691.
68Ibid., p. 677. 138
to be a useful tool to alleviate the problem of collin-
earity; however, its application as an analytical tool
is limited because of the sensitivity of the solution to
variations both in the number of observations and in the 6 9 number of original variables. Also, the selection of those variables and the interpretation of the results of
factor analysis are absolutely dependent on the modeller's judgment. Concerning this point, Isard, for instance, says that "the analyst must bear in mind the extent to which factor analysis cannot eliminate his responsibility for sound reasoning and judgment, and in many cases cannot eliminate the need to resort to arbitrary procedures.
Briefly put, factor analysis is not nearly as objective 70 as appears to the unsophisticated analyst." Although Vickerman regards the attraction- accessibility index as most satisfactory in many cases.
6 9 Factor analysis has a long history in psychology, and to a lesser extent in sociology and political science. Recently, this method has frequently been used among social scientists. The single most distinctive characteristic of this method is its data-reduction capacity; namely, it offers a fruitful approach to condensing voluminous sets of data into relatively few useful indices or dimensions. It is often used as an effective tool to delineate regions within a system which have firm theoretical foundations. As many statistical tools, it can serve as a partial test of an hypothesis or reflect the adequacy of the charac teristics initially selected as relevant, etc. (Isard, 1960). 70 Walter Isard, Method of Regional Analysis: An Introduction to Regional Science, Cambridge: The MIT Press, 1960, p. 305. 139 independent attraction-accessibility indices may be calculated by the Hansen method. Hansen calculated the attraction-accessibility indices for three different attractions, i.e., employment, shopping opportunities, and residential activity (Hansen, 1959). In most cir cumstances, however, it would be more satisfactory if the combined attraction-accessibility indices are produced in connection with spatial interaction models. Taking into account these points, this research dealt with two different attraction-accessibility indices. The first to be examined were the accessibilities of each node (the
Lansing Metropolitan Area is divided into 192 nodes and the detail is discussed in the following section of this chapter) to decentralized shopping opportunities (to five major retail centers in the Lansing Metropolitan Area) and employment opportunities (six major employment centers).
The second to be examined were the accessibilities of the above 192 nodes to more generalized urban functions which are assumed to be represented by the population in cities of known size. The underlying reason for the selection of the population in cities of known size is that the population of such cities can be assumed to reflect the urban functions which each of those cities possesses.
The urban functions here are job and shopping opportunities, amenities such as cultural and academic events, cultural and amusement opportunities such as theaters, movies, 140 museums, restaurants, sports, and recreational opportunities and facilities. It is true that people are concerned with the environmental quality around them; however, they also need to have such urban functions as described above and various maintenance services such as gas, electricity, sewage treatment, garbage collection, fire and police protection, and good school systems, which require some amount of supportable population.
Attraction-Accessibility Indices to Analyze the Population Settlement Patterns in the Lansing Metropolitan Area
In the middle of the year 1981, the (Lansing)
Tri-County Regional Planning Commission released a summary of its investigation concerning the population trend within the Lansing Metropolitan Area, based on the 19 80 United 71 States Census of Population. According to the summary, the suburban areas within the Lansing Metropolitan Area have grown much faster than such cities as Lansing, East
Lansing, St. Johns, Charlotte, Eaton Rapids, Potterville, and Mason and also such villages as Dimondale and Dansville.
In the summary, forty-eight townships in the Lansing
Metropolitan Area were divided into three groups according to the level of population growth during the past ten years (Figure 23). As shown in Figure 23, only two
71 Jane Garrick, "Lansing a Loser in 198 0 Census," The Lansing State Journal, April 19, 1981. 141
1 Lebanon Kalatno 2 Essex Carmel 3 Greenbush Eaton 4 Duplain Eaton Rapids 5 Dallas Bellevue 6 Bengal Walton 7 Bingham Brookfield 8 Ovid Hamlin 9 Westphalia Lansing 10 Riley Meridian 11 Olive IK Willlamston 12 Victor Locke 13 Eagle Delhi 14 Watertown Alaiedon 15 Dewitt Wheat£ield 16 Bach Leroy 17 Sunfleld Aurelius 18 Roxand mmmm Vevay 19 Oneida Ingham 20 Delta Whiteoak 21 Vermontville Onondaga 22 Chester Leslie 23 Benton Bunkerhill 24 Windsor Scockbrldge
LU«
uzu
a saai
High growth □ Moderate growth Loss
Clinton County ( 1-16) Lansing Metropolitan Area Eaton County (17-32) — Ingham County (33-48) Figure 2 3.— Population Growth During the 1970's in the Lansing Metropolitan Area. SOURCE: 198 0 U.S. Population Census. 142
townships experienced a population loss during the 1970*s.
Those were Lebanon Township, which is located in the northwestern corner of Clinton County, and Lansing Township, wherein the City of Lansing occupies most of the land area.
At the city level, Dewitt is the only exception; it experienced a population increase during the 1970's.
Thirteen out of the 48 townships scored high growth and nine out of those thirteen townships accommodate one of those cities or villages mentioned above. On the county level, Eaton County had the biggest population gain of
28 percent, from 68,8 92 to 88,337. Clinton County also scored a strong gain, growing 15 percent, from 48,49 2 to
55,893 residents. Ingham County, on the other hand, registered only a 4 percent population increase, going from 2 61,039 to 272,437 residents. This strong growth of non-urban townships implies that the population has been dispersing evenly within the Lansing Metropolitan Area more than ever. Taking this trend of non-urban township growth into account, the township was designated as the demand node where people who want to utilize certain facilities or the opportunities such as employment or shopping live. Further, if this non-urban township growth continues, even the existing highway system (in terms of the availability of interchanges to these new residents) might have to be reconsidered. Fortunately, however, the street and road system within the Lansing Metropolitan Area is based on the rectangular survey system which is
ordinarily seen in the Midwest region of the United States;
that is, most of the area within the Lansing Metropolitan
Area is subdivided into sections or subareas of one square mile (1 mile x 1 mile) by the basic section line roads which run in the directions of north to south and east to 72 west (Figure 24). This systematic street system will be
utilized more and more by those residents who dispersed
from the central urban areas to suburban areas. This part of the research, accordingly, will be based on these two
facts: the population increase in non-urban townships and the existence of the rectangular street system within the
Lansing Metropolitan Area.
To figure out the attraction-accessibility indices, the Lansing Metropolitan Area was divided into 192,3x3 mile, subareas, rather than 4 8, 6 x 6 mile, subareas (the area of the township) or 1,728, l x l mile, subareas (the area of the section). The reason for the adoption of
192 subareas was to obviate the need for extensive and expensive calculation processes which were likely to result from the adoption of 1,728 subareas and to secure a
72 This rectangular survey system is applied . in the thirty public domain states and also in part of some other states. In the system, a county which has the area of 24 x 2 4 mile square is composed of 16 townships. A township (6x6 mile square) consists of 36 sections. A section (lxl square mile) is equivalent to 64 0 acres and the section is usually divided into 4 quarter sections. 144
COUNTIES (14 J V Ull, tlttlll
I |1 l'< N h MI Jifijj
MICHIGAN 4 3 4 3 2 1
WORLD 7 8 a 10 11 12 ia IT 1 4 13 1 4 1 3
it S O SI 2 2 2 3 2 4
3 0 s » 2 1 27 2 4 j 24
31 31 3 3 3 * 3 8 I 34
MERIDIAN TOWNSHIP T A N T 4 N [ T 4 N T A N M S W ^ RIW 1 RLE M S C (34*0** S« Mil* tltllii/
T t v t k l * . I 1*1 T 3 N T J N } T J N r j n M S W R I W <| MIC MSE ii -i
TIN T i N -1 T I N t s n M S w Ml W Z | MIC MSC wi iJ «1 i J
T i n T I N w\ T I N r 1 n M S w M I W M I C R 2 C
l A t C S LI N I ______M\ ONE SECTION (4 Oy*iT** lieiltM)
INGHAM COUNTY (14 Tb w m Jh m )
A C O U K T T ( X 4 ■ 2 4 mill ■ \ * 18 T O W N S H I P S 4 TOWNSHIP K ■ • mill!) ■ 31 SECTIONS
A 3CCTI0K {I M - "11*1 * « 4 Q ACftC* A 1/4 SECTION • 1/4 > 4 . mil* " 100 ACHES
A N ACftE ■ 4 9t 3 6 0 *4. T*«l
Figure 24.— The Federal Land Survey System (The Rectangular System).
SOURCE: Myles G. Boylan, Urban Design Papers. (Mimeo). East Lansing: MSU, p. UPD 5. 145 reliability and accuracy of analysis which could not have been assured by the adoption of 48 large subnodes. In fact, the aggregation or disaggregation of the area is very crucial for spatial analysis. Zone or area config uration is often the key to the success of urban and regional analyses; that is, an excessive aggregation results in a loss of detail and an excessive disaggregation results in prohibitive data collection and calculation costs. The adoption of 192 subnodes in this research, however, is not likely to degrade the reliability and accuracy of the analysis because the land uses and land covers in the Lansing Metropolitan Area are mostly homogenous; that is, most of the land uses and land covers are rural, residential, or agricultural land uses.
The formula used to calculate the attraction- accessibility indices is shown below:
m Ej A. = I ---- where A^ = accessibility at node i 1 j=l D. ^ Ej = size of activity (attraction) 1U at node j; i.e.., number of people, jobs, capacity of school, etc. D^j= distance disutility between node i and j k = an exponent describing the effect of the travel time or distance between node i and j (distance disutility parameter) m = number of destinations; i.e., number of facility locations, job locations, etc. 146
Accessibility to Shopping Opportunities
In the Lansing Metropolitan Area there are five major retail centers. These are 1) Lansing CBD,
2) East Lansing CBD, 3) Frandor Shopping Center, 4) Lansing
Shopping Mall, and 5) Meridian Shopping Mall. The total amount of retail sales in these five retail centers has
reached approximately $300 million, which is approximately
15.5 percent of the total retail sales volume for the
Lansing Metropolitan Area. The following figures are the amount of sales at each of five major retail centers as of 1977, investigated and released by the (Lansing)
Tri-County Regional Planning Commission in 1981 (in 73 thousands).
Lansing CBD - $ 44,957
East Lansing CBD - $ 25,119
Frandor - $148,892*
Lansing Mall - $ 46,683
Meridian Mall - $ 29,038 * Including the amount of sales from STORY-OLDS which is the largest Oldsmobile Dealer in the United States.
The locations of these five major retail centers are shown in Figure 25. The accessibility of each of the
73 The (Lansing) Tri-County Regional Planning Commission, Regional Fact Book for the Tri-County Region, Lansing: Tri-County Regional Planning Commission, June 1981, p. Economic Base 15. 147
imiles
1 2 5 6 9 10 13 14
3 4 7 a 11 12 15 16 42
17 18 21 22 25 26 29 30 ^Lansing Mall (45,000)
^Lansing CBD (25,200) 19 20 23 24 27 28 31 36 // ^Frandor (149,000) 33 34 37 38 41 42 x// Ease Lansing CBD (46,700) 35 36 39 40 43 /, /•/ Meridian Mall (29,100) 30 54 49 50 53 ! j 7 I/I// r\ 51 52 55 66 A y 6 0 i 64 / 24 AlL 65 66 69 70 73 74 77 13 f 137 138 141 142
67 68 71 72 75 76 79 80 131 132 135 139 140 143 144 18 81 82 85 86 89 90 93 94 145 146 149 150 153 154 157 158
83 84 87 88 91 92 95 96 147 148 151 152 155 156 159 160 12 169 170 173 174 97 98 101 102 105 106 109 110 161 162 165 166
99 100 103 104 107 108 111 112 163 164 167 168 171 172 175 176
113 114 117 118 121 122 125 126 177 178 181 182 185 186 189 190
115 116 119 120 123 124 127 128 179 180 183 184 187 188 191 192
miles 12 13 24 30 36 42 48
Clinton County { 1-64) Lansing Metropolitan Area — Eaton County ( 65-12 8) Ingham County (12 9-192)
Figure 25.— Locations of Shopping Opportunities in the Lansing Metropolitan Area and the Levels of Attractive ness (the Amount of Retail Sales in Thousand Dollars).
SOURCE: Tri-County Regional Planning Commission, Regional Fact Book for Lansing and Tri-County Region, 1981, p. Ec-15. 148
192 demand nodes to those five major retail centers was calculated and the results are shown in Appendix VII.
The value of exponent, k, adopted for this calculation ranges from 0.5 to 3.0.
The accessibility at each of the 192 demand nodes, then, was reaggregated into 48 township nodes to examine the statistical relationship between those accessibilities and the number of residents in each of 48 township nodes.
The results are shown in Table 14. As shown in
Table 14, the correlations between the two variables
(township nodal accessibilities and the number of their residents) are mostly high. The correlation coefficients range from 0.809, at k - 0.5, to 0.962, at k = 2.0. These high correlation coefficients indicate that there are strong linear relationships between the above two variables.
Statistically, a correlation coefficient equal to 0.962 means that about 92.5% (0.9622 = 0.9254) of the variability of the populations at 4 8 township nodes can be explained by a linear relation.
In this part of the study, the highest correlation coefficient was obtained at the value of 2.0 for exponent k. In most of the cases, the exponent values decrease as trips become more important; namely, for school trips,
2.0; for shopping trips, 2.0; for social trips, 1.1; for 74 work trips, 0.9; etc. The accessibility is the inverse
^Hansen (1959) , p. 74. 149
TABLE 14.— Nodal Accessibility to Shopping Opportunities at Township Base and Statistical Relation ship between Accessibility and Population.
k - 0.5 k - 1.0 k - 1.5 k - 2.0 k - 2.5 k - 3.0
Access Access Access Access Access Access Township Population ibility ibility ibility ibility ibility ibility
Lebanon 697 197,208.0 33,096.4 5,575.8 943.0 160.1 27.4 Essex 1,688 216,470.2 39,947.6 7,413.7 1,383.8 259.1 49.0 Greenbush 1,929 233,623.0 46,459.0 9,275.6 1,859.1 373,9 75.4 Duplain 2,330 225,409.0 43,234.6 8,323.5 1,608.4 311.9 60.6 Dallas 2,288 216,470.2 39,947.6 7,413.7 1,383.8 259.6 49.0 Bengal 1,067 242,998.4 50,510.2 10,596.1 2,243.8 479.8 103.6 Bingham 9,747 267,822.5 61,228.3 14,091.5 3,264.1 760.8 178.3 Ovid 3,241 255,596.6 55,725.7 12,224.5 2,698.3 599.2 L33.9 Westphalia 2,350 242,998.4 50,510.2 10,596.1 2,243.8 479.8 103.6 Riley 1,547 283,332.7 69,247.5 17,238.7 4,375.6 1,133.4 299.9 Olive 2,111 324,440.8 90,531.2 25,681.4 7,348.5 2,135.5 ' 628.4 Victor 2,287 303,161.7 78,881.9 20,783.8 5,546.2 1,499.1 410.5 Eagle 2,060 283,332.7 69,247.5 17,238.7 4,375.6 1,133.4 299.9 Watertown 3,602 360,235.5 116,501.2 40,456.0 15,276.8 6,312.0 2,842.1 Deyitt 13,203 456,029.9 186,635.7 80,966.6 37,164.8 17,958.3 9,067.3 Bach 5,746 399,699.0 141,307.9 52,451.2 20,560.0 8,547.4 3,773.1 Suofield 1,998 229,423.1 44,891.2 8,839.1 1,751.7 349.5 70.3 Roxand 1,975 261,892.3 58,770.5 13,339.2 3,064.2 712.8 168.1 Oneida 10,298 315,551.3 86,590.5 24,460.7 7,137.9 2,158.1 677.1 Delta 28,262 446,769.6 196,421.2 106,200.9 71,591.1 57,367.5 51,098.0 Vermontville 1,942 207,601.9 36,660,5 6,496.8 1,155.4 206.2 37.0 Chester 1,622 230,420.6 45,248.8 8,935.4 1,774.8 354.7 71.5 Benton 3,907 263,269.3 59,320.9 13,504.6 3,108.4 724.0 170.7 Windsor 6,078 317,633.8 66,311.2 24,792.7 7,240.5 2,187.9 685.5 Kalamo 1,683 190,513.2 30.B41.7 5,005.1 814.2 132.7 21.6 Carmel 8,769 207,601.9 36,660.5 6,496.8 1,155.4 206.2 37.0 Eaton 4,965 230,420.6 45,248.8 8,935.4 1,774.8 354.7 71.5 Eaton Rapids 3,725 263,269.3 59,320.9 13,504.6 3,108.4 724.0 170.7 Bellevue 2,725 177,080.2 26,629.4 4,011.6 605.4 91.6 13.8 Walton 3,205 190,513.2 30,841.7 5,005.1 814.2 132.7 21.6 Brookfield 1,380 207,601.9 36,660.5 6,496.8 1.155.4 206.2 37.0 Hamlin 5,803 230,420.6 35,138.8 8,935.4 1,774.8 354.7 71.5 Lansing 116,493 644,272.3 404,247.3 291,315.5 235,769,3 207,379.0 192,404.3 Meridian 77,603 538,410.8 282,177.7 173,360.1 124,282.5 100,887.4 89,191.1 Williamston 5,472 356,747.7 111,794.1 36,562.3 12,581.5 4,590,1 1,784.1 Locke 1,456 285,853.5 70,174.7 17,465.4 4,410.6 1,131.1 294.9 Delhi 36,722 378,602.1 161,936.4 41,295.9 14,049.9 4,876.4 1,725.6 Alaledon 2,845 356,866.7 112,976.8 37,864.6 13,608.5 5,300.8 2,246.6 Wheatfield 3,004 298,341.3 77,994.6 21,267.7 6,110.2 1,866.6 609.5 Leroy 3,413 251,238.8 54,225.0 11,879.7 2,846.6 600.7 139.2 Aurelius 2,460 295,695.7 74,621.B 13,959.9 4,850.3 1,249.4 324.0 Vevay 9,132 282,682.3 68,697.3 16,953.1 4,250.7 1,984.0 281.5 Ingham 1,974 251,238.8 54,225.0 11.B79.7 2,846.6 600.7 139.2 White Oak 1,096 222,006.5 42,095.0 8,046.0 1,551.2 302.0 59.3 Onondaga 2,289 251,482.4 53,770.5 11,549.9 2,488.3 537.9 116.7 Leslie 4,300 242,809.4 50,347.6 10,518.9 2,214.9 470.1 100.6 Bunker Hill 1,794 222,006.5 42,095.0 8,046.0 1,551.2 302.0 59.3 Scockbrldge 2,914 201,286.9 34,513.6 5,948.1 1,030.6 179.6 23.9 0.6201749 Slone 0.1764784 0.2651835 0.3925248 0.5005031 0.5748979 3313.6128 4027.0466 Y- interception -41157.3B -12106.17--2169.367 1835.4870 _ a p 1 r n OHfiOA? O.9620010 0.9596237 0.9539215 Corr. Coeff, (r) 0.8089657 0.9183882 150 function of the distance between the nodes i and j.
Accordingly/ the less the value of exponent/ k, the more the influence of the distance. That is, people are willing to travel farther to work than they are willing to travel for any other of the purposes described above. According to Hansen's study, those exponents figured out from the data in the Washington, D.C. metropolitan area were for work trips, 2.20; for social trips, 2.35; for shipping trips, 3.00. These values are peculiar to individual communities, but ordinarily range between 0.5 and almost
3.0 (Hansen, 1959). The value of the exponent for shopping opportunities within the Lansing Metropolitan Area, 2.0, which is obtained in this study, accordingly, should be regarded as an appropriate figure.
Accessibility to Employment Opportunities It is commonly said that the Lansing Metropolitan
Area's economy is based on the "big three:" automobile production, state government, and education. In fact,
General Motors' Oldsmobile Division, the state government,
Michigan State University, and a fourth influential employment sector, local governments, together account for almost half of all employment in the Lansing Metro politan Area. The State Government employed approximately
39.000 workers in 1980, including about 10,000 Michigan
State University employees. Approximately 24,000 workers 151 are employed by Oldsmobile Division of General Motors, and local governments employed about 24,000 workers in 1980.
The remaining half are employed by such sectors as retail trade, services, wholesale, transportation, machinery, etc.
No detailed data for these sectors are available for the year 19 80. However, another publication done by the
(Lansing) Tri-County Regional Planning Commission in 1979 75 illustrates six key employment locations. These are
Lansing CBD, the area between Lansing CBD and East Lansing
CBD, East Lansing CBD, the Oldsmobile Plant in Downtown
Lansing, and the Lansing and Meridian Shopping Mall areas.
Although there are no concrete data concerning the numbers of employees in these six locations, this estimate was made by the writer based on the employment data in 1974.
(The above publication released in 1979 by the Commission also uses the data for the year 1974.) The assumed number of employees in each of the above six employment locations is shown in Figure 26.
The accessibility at each of the 192 nodes to these six employment locations was calculated; the results are shown in Appendix VIII. As in the previous section, the 192 nodes were reaggregated into 4 8 township nodes and the statistical relationships between the 4 8 township nodal
75The (Lansing) Tri-County Regional Planning Commission, Long Range Street and Highway Plan for the Tri-County Region, Lansing: Tri-County Regional Planning Commission, April 1979. 152
1 2 5 6 9 10 13 14
3 4 7 8 11 12 15 16 42 Lansing Mali Area/Delta 17 18 21 22 25 26 29 3 0 / 'Township/Part of Lansing (18,000) Downtown Lansing Area 19 20 23 24 27 28 31 A / (35,500) 36 Oldsmobile Plant Area 33 34 37 38 41 42 (24.000) // Lansing/Eas Lansing Corridor (15.000) 35 36 39 40 43 ,Downtown East Lansing 30 /// / > Including M.S.U. (24.000) 49 50 53 54 .Meridian Mall Area /T // V/(6 ,000) / 60/ '64 j 51 52 55 y j y 24 L .41 142 65 66 69 70 73 74 J 137 138 ” 1■■i■ 67 68 71 72 75 76 * 80 132 .» 139 140 143 144 IB ■ ■ 81 82 85 86 89 90 93 94 145 146 149 150 153 154 157 158
83 84 87 88 91 92 95 96 147 148 151 152 155 156 159 160 12 169 170 173 174 97 93 101 102 105 106 109 110 161 162 165 166
99 100 103 104 107 108 111 112 163 164 167 168 171 172 175 176
190 113 114 117 118 121 122 125 126 177 178 181 182 185 186 189
115 116 119 120 123 124 127 128 179 180 183 184 187 188 191 192
12 18 24 30 36 42 48 miles
Clinton County ( 1-64) Lansing Metropolitan Area — Eaton County ( 65-128) Ingham County (129-192)
Figure 26.— Locations of Employment in the Lansing Metropolitan Area and the Levels of Attractiveness (the Number of Employment).
SOURCE: Tri-County Planning Commission, Projected Development Patterns Year 2000, p. 33. Adjusted by Shun'ichi Hagiwara. 153 accessibilities and their population were also examined.
The results are shown in Table 15. As shown in Table 15, the correlation coefficients obtained are again mostly high, ranging from 0.8 09, at k = 0.5, to 0.954, at k = 2.0.
The highest correlation coefficient was obtained again at k — 2.0, and this value should also be regarded as an appropriate one. These two studies indicate that the population in the townships in the Lansing Metropolitan
Area decrease in proportion to the inverse-square of the distance from the five major retail centers and the six major employment centers.
Using this relationship, it is possible to estimate the residential growth in any township in the Lansing
Metropolitan Area if new or additional employment or shopping opportunities were added anywhere within the
Lansing Metropolitan Area.
Accessibility to Urban Functions
Population movement during the past decade (1970-
198 0) in the Lansing Metropolitan Area can be labelled the "non-urban township growth" in which people are moving into the "non-urban townships" around smaller cities.
Several reasons for this have already been mentioned, but it is occurring primarily because of lower taxes and security, etc. in the non-urban areas and still can allow people to enjoy the necessary services for comfortable 154
TABLE 15.— Nodal Accessibility to Employment at Township Base and Statistical Rela tionship between Accessibility and Population.
k - 0.5 k - 1.0 k - 1.5 k - 2.0 k - 2.5 k - 3.0 Access- Access Access Access Access Access Township Population lbllitv ibility ibility ibility ibility ibility
Lebanon 697 82,931.9 14,084.9 2,400.4 410.5 70.4 12.2 Essex 1,688 91,252.4 17,080.5 3,213.4 607.7 115.4 22.0 Greenbush 1,929 96,465.1 19,052.6 3,775.2 750.4 149.7 29.9 Duplain 2,330 91,122.7 17,031.6 3,200.1 604.5 114.8 21.9 Dallas 2,288 91,252:4 17,080.5 3,213.4 607.7 115.4 22.0 Bengal 1,067 102,808.6 21,749.2 4,639.4 997.9 216.5 47.3 Bingham 9,747 110,327.9 24,979.4 5,686.5 1,302.0 299.7 69.3 Ovid 3,241 102,627.9 21,675.9 4,618.5 993.0 215.6 47.3 Westphalia 2,350 102,808.6 21,749.2 4,639.4 997.9 216.5 47.3 Riley 1,547 120,612.2 30,167.9 7,671. 7 1,984.5 522.5 139.9 Olive 2,111 132,977.3 36,515.9 10,147.8 2,854.2 812.6 234.1 Victor 2,287 120,360.1 30,068.3 7,649.6 1,984.0 525.1 141.8 Eagle 2,060 120,612.2 30,167.9 7,671.7 1,984.5 522.5 139.9 Watertown 3,602 155,319.2 51,885.4 18,443.6 7,038.2 2,897.6 1,285.5 Dewitt 13,203 183,464.2 72,077.7 29,858.1 13,074.9 6,046.9 2,940.1 Bath 5,746 155,295.6 52,244.4 18,903.4 7,427.6 3,176.1 1,467.8 Sunfield 1,998 97,815.9 19,642.2 3,967.5 806.0 196.8 33.8 Roxand 1,975 112,556.8 26,124.2 6,126.6 1,451.7 347.7 84.0 Oneida 10,298 137,710.0 39,642.2 11,700.7 3,544.1 1,102.2 352.0 Delta 28,262 203,652.0 96,261.0 53,283.1 34,892.2 26,501.0 22,448.0 Vermonvllle 1,942 88 ,868.8 16.189.2 2,962.1 544.4 100.4 18.6 Chester 1,622 99,398.7 20,303.2 4,175.9 864.7 180.3 37.8 Benton 3,907 115,071.7 27,361.5 6,587.5 1,605.7 396.3 98,8 Windsor 6,078 142,812.4 42,906.4 13,300.6 4.254.B 1,403.6 476.7 Kalamo 1,683 81,140.9 13,477.8 2,245.6 375.3 63.0 10.6 Carmel 8,769 88 ,868.8 16,189.2 2,962.1 544.4 100.4 18.6 Eaton 4,965 99,39B.7 20,303.2 4,746.8 864.7 180.3 37.8 Eaton Rapids 3,725 115,071.7 27,361,5 6,587.5 1,605.7 396.3 98.8 Bellevue 2,725 75,148.5 11,551.3 1,779.7 274.9 42.5 6.5 Walton 3,205 81,140.9 13,477.8 2,245.6 375.3 63.0 10.6 Brookfield 1,380 88,868.3 16,189.2 2,962.1 544.4 100.4 18.6 Hamlin 5.B03 99,398.7 20,303.2 4,175.9 864.7 180.3 37.8 Lansing 116,493 271,662.8 172,083.4 124,595.1 101,000.1 SB,852.3 82,413.2 Meridian 77,603 204,969.7 100,963.8 60,801.5 44,272.5 37,004.0 33,600.2 Williamston 5,472 139,540.7 41,107.8 12,621.7 4,072.8 1,392.6 507.9 Locke 1,456 114,144.3 26,940.6 6.450.2 1,568.5 387.8 97.6 Delhi 36,722 166,420.0 59,047,2 22,078.9 8,769.2 3,717.3 1,681.2 Alaiedon 2,845 142,683.9 43,154.4 13,683.0 4,600.0 1,659.3 648.2 Wheatfield 3,004 119,632.2 29,930.8 7,726.8 2,076.9 587.3 176.5 Leroy 3,413 101,978.8 21,446.7 4,563.6 984.1 215.3 47.8 Aurelius 2,460 126,107.8 32,833.0 8,653.6 2,310.2 1,525.0 171.5 Vevay 9,132 114,804.7 27,246.6 6,556.1 1,601.0 396.9 100.1 Ingham 1,974 101,978.8 21,446.7 4,563.6 984.1 215.3 47.8 White Oak 1,096 90,622.3 16,861.5 3,157.7 595.4 113.1 21.6 Onondaga 2,289 106,306.9 23,192.3 5,088.8 1,123.0 249.4 55.8 Leslie 4,300 99,216.9 20,227.3 4,213.0 859.0 179.0 37.6 Bunker Hill 1,794 90,622.3 16,861.5 3,157.7 595.4 113.1 21.6 StockbridRe 2.914 82.440.9 13.925.7 2.362.1 402.4 68.8 11.8
Slope 0.4298723 0.6429734 0.9372413 1.1863299 1.3569475 1.4638146 Y-interception -42000.18 -12367.97 -2161.168 1947.6165 3530.2735 4114.2040 Corr. Coeff. (r) 0.8087223 0.9009588 0.9372413 0.9543566 0.9511577 0.9478019 155
7 6 lives even in smaller cities. The necessary services,
however, may better be translated as the "urban functions"
described earlier.
Taking into account this recent trend of population
movement, the smaller cities with more than a 2,5 00
population were selected, in addition to such larger
cities as Lansing and East Lansing, as the destination
nodes. Those cities selected as the destination nodes and
their populations are as follows:
Lansing 116,500 (most of the City of Lansing excluding the parts located in Dewitt, Delhi, and Delta Township areas) East Lansing 77,600 (East Lansing and part of the City of Lansing) St. Johns 7,4 00
Dewitt 3,17 0
Grand Ledge 6,920
Charlotte 8,250
Eaton Rapids 4,510
Mason 6,02 0
Williamston 2,980
The figures above are round numbers; the locations of those nine cities are shown in Figure 27. The results of the study are shown in Appendix IX.
As in the previous sections, the accessibility of the 192 nodes were reaggregated into 4 8 township nodes to examine
76Jane Garrick (1981). 156
4 g miles
St. Johns (7,400)
Grand Ledge (6,920) Dewitt (3,170)
Eaton Rapids (4,510)i Lansing (116,300)
Charlotte (8,250) East Lansing (77,600)
Mason (6,020)
•\W11 llama ton (2,980)
miles
Clinton County ( 1-64) — Eaton County ( 65-128) E Ingham County (129-192)
Figure 27.— Locations of Nine Destination Nodes (Nine Cities with Population More Than 2,500).
SOURCE: Tri-County Regional Planning Commission, 1980 Census Results. Adjusted by Shun'ichi Hagiwara. 157 the statistical relationship between the accessibility to urban functions at each township and its population. The results are shown in Table 16. The correlation co efficients obtained in this part of the study range from
0.806, at k = 0.5, to 0.969, at k = 2.0. Again, fairly and very high linear relationships were found in this case, and the correlation coefficient, 0.969, obtained here was the highest among those obtained in the accessibility studies for shopping and employment opportunities and urban functions.
Hence, a study has been made for three different attractions: shopping opportunities, employment oppor tunities, and urban functions. In the writer's view, the third attraction (urban functions) is the best measure to investigate the recent population movement in the Lansing
Metropolitan Area and in most of urban areas in the United
States. According to Jason Whitler of the (Lansing) Tri-
County Regional Planning Commission, the strong growth of non-urban townships was also evident in neighboring sections of Shiawassee and Ionia Counties. It is safe to say that most of urban areas in the United States have experienced a similar phenomenon during the past decade. By taking into account the urban functions in smaller cities, it becomes possible to approach the recent problem of population movement to rural areas which has been labelled as an "exurbanization" movement. In this regard, the urban 158
TABLE 16.— Nodal Accessibility to the Urban Functions at Township Base and Statistical Relation ship between Accessibility and Population.
k - 0.5 k - 1.0 k - 1.5 k - 2.0 k “ 2.5 k - 3.0 Access Access Access Access Access Access Township Population ibility ibility ibility ibility ibility ibility
Lebanon 697 15,977.8 2,774.9 491.0 89.0 16.6 3.4 Essex 1,688 17,531.8 3,392.2 687.2 149.3 35.7 9.6 Greenbush 1,929 18,900.3 4,043.5 958.5 270.0 95.0 40.9 Duplain 2,330 17,750,1 3,442.0 682.8 139.3 29.5 6.5 Dallas 2,288 17,592.7 3,377.4 663.8 134.5 57.8 6.3 Bengal 1,067 19,820.2 4,422.6 1,077.2 303.3 103.9 43.1 Bingham 9,747 22,055.4 5,918.9 2 ,121.0 1,167.6 900.2 811.7 Ovid 3,241 20,094.0 4,443.1 1,013.4 240.1 59.8 15.7 Westphalia 2,350 19,553.6 4,157.1 898.2 197.5 44.4 10.2 Riley 1,547 22,478.5 5,529.4 1,386.8 356.1 93.4 25.0 Olive 2,111 25,399.3 7,130.0 2,065.8 620.5 194.9 64.9 Victor 2,2d7 23,329.5 5,993.9 1,579.2 421.0 117.5 33.1 Eagle 2,060 22,780.4 5,766.5 1,539.3 445.1 144.4 53.9 Watertown 3,602 27,B59.5 8,688.6 2,827.8 959.4 339.0 124.7 Dewitt 13,203 34,724.4 14,040.4 6,181.2 2,984.2 1,598.3 961.6 Bath 5,746 30,443.0 10.B36.3 4,222.1 1,797.0 827,9 407.0 Sunfleld 1,998 13,288.8 3,627.8 728.9 148.6 30.8 6.4 Roxand 1,975 21,017.4 4,846.7 1,149.3 281.7 71.9 19.2 Oneida 10,298 25,512.4 7,636.4 2,751.5 1,353.7 928.1 786.9 Delta 28,262 33,305.9 12,940.6 5,461.9 2,484.5 1,203.4 612.5 Vermontville 1,942 16,848.0 3,103.0 587.8 115.8 24.1 5.4 Chester 1,622 19,085.1 4,138.9 1,001.2 289.3 104.0 45.3 Benton 3,907 21,392.1 5,039.1 1,228.6 312.8 84.0 24.0 Windsor 6,078 24,879.8 6,764.7 1,874.6 529.0 151.8 44.3 Kalatno 1,683 15,610.8 2,799.0 534.9 115.2 28.9 8.4 Carmel 8,769 18,168.7 4,400.5 1,723.3 1,127.7 958.2 893.2 Eaton 4,965 19,349.1 4,264.4 1,046.9 304.3 108.8 46.8 Eaton Rapids 3,725 21,472.4 5,103.1 1,274.9 344.6 104.9 37.4 Bellevue 2,725 14,547.1 2,321.0 383.2 66.2 12.1 2.5 Walton 3,205 15,910.8 2,846.2 551.7 120.5 30.5 8.9 Brookfield 1,380 17,206.1 2,983.5 659.7 142.5 33.8 9.0 Hamlin 5,803 19,184.3 4,408.0 1,373.2 709.0 543.6 492.7 Lansing .116,493 47,124.0 28,381.5 20,026.6 16,024.8 14,003.8 12,943.2 Meridian 77,603 39,374.4 20,602.7 13,436.8 10,454.4 9,113.0 8,468.9 Williamston 5,472 26,385.8 7,987.3 2,685.8 1,084.1 575.0 403.8 Locke 1,456 21,551.9 5,140.0 1,276.6 336.0 96.7 31.6 Delhi 36,722 28,808.8 9,159.9 2,994.8 1,005.2 345.7 121.6 Alaiedon 2,845 26,486.8 7,883.8 2,469.9 817.9 287.5 107.7 Wheatfield 3,004 22,221.3 5,457.1 1,393.4 375.4 109.5 35.7 Leroy 3,413 19,092.7 3,977.0 846.3 185.2 41.9 10.0 Aurellua 2,460 23,521.4 6,105.0 1,654.1 478.4 152.3 54.9 Vevay 9,132 22,480.6 6,002.9 2,039.1 1,033.4 907.2 666.4 Ingham 1,974 19,465.0 4,159.8 918.0 211.5 51.6 13.4 White Oak 1,096 17,260.5 3,235.3 616.0 119.4 23.7 4.8 Onondaga 2,289 20,165.0 4,498.6 1,062.4 277.5 84.9 31.4 Leslie 4,300 19,109.3 4,012.1 871.3 198.0 47. 7 12.4 Bunker Hill 1,794 17,335.0 3,267.2 630.5 122.5 24.6 5.1 Stockbrldge 2,914 15.746.5 2.682.3 461.8 80.4 14,3 2.5
Slope 2.5405034 3.8855811 5.7302372 7.2291078 8.2021434 8.7836290 Y-intersectlon -47645.04 -15133.82 -3361.197 932.26640 2718.7074 3463.2180 Corr. Coeff. (r) 0.8055598 0.9062104 0.9588910 0.9690317 0.9645855 0.9590642 159 functions are considered to be the prime attraction for people and the key factor in locational decisions made by people and also entrepreneurs in the following study.
Before getting into the next stage of the research concerning the site selection of a Lansing HSIPT system terminal and the probable impacts flowing outward from the site to the rest of the Lansing Metropolitan Area, we may consider one of the interesting arguments concerning the 77 concept of accessibility offered by John Symons. He rejects the concept of nodal accessibility which has been discussed in the previous text. According to him, the nodal accessibility which is often higher in the CBD and lower in the suburbs is a superficial one. He asserts that one should look at the distribution of accessibility per person (per capita accessibility) at each node. By applying this per capita accessibility concept, according to him, one will find that highly accessible CBD nodes with high population densities often have as low or lower per capita accessibility than low accessibility, suburban low density nodes. Although Symons developed the per capita accessibility concept to examine equity and efficiency in public facility location, and though his
77 John G. Symons, Jr., "Some Comments on Equity and Efficiency in Public Facility Location Models," ANTIPOLE, 3, 1, November 1971, p. 64. Also, see David Harvey, Social Justice and the City, Baltimore: The John Hopkins University Press, 1973, pp. 96-118. 160
concept is not directly related to residential or industrial
land use models, it still might be possible to find out
certain standards for the developability of land by using
his method. In the following section, the writer applies
this per capita accessibility concept to the Lansing
Metropolitan Area to determine if two such radically different concepts as real and superficial accessibilities actually exist.
Concept of Per Capita Accessibility: How Does It Work?
Symons1 comments concerning the nodal accessibility concept can be summarized in the following two comments:
1) per capita accessibility at the CBD will be as low or lower than the one at the suburban areas with a low density of population; and 2) there will be a strong statistical relationship between per capita accessibility and per capita income. In this section, these two comments offered by John Symons are investigated and, if not found useful, alternative ways to utilize the concept of per capita accessibility will be sought.
Symons* concept of per capita accessibility is expressed as follows:
Ai . . . Apci = ---- where Apci = per capita accessibility at Popi node i A^ = nodal accessibility at node i Popi = population at node i 161
Table 17 shows the nodal accessibility to urban
functions in the Lansing Metropolitan Area at the 48
township nodes, population in the 48 township nodes, per
capita accessibility at each of the 4 8 township nodes
based on the above formula, and per capita income at each
of the 48 township nodes. The means and standard deviations
are calculated for the nodal accessibility, per capita
accessibility, and per capita income. The distribution of
nodal accessibility is shown in Figure 28 and the distri bution of per capita accessibility is shown in Figure 29.
As shown in Figure 28, the disparity between the nodes with the highest nodal accessibility and the nodes with the lowest accessibility is very high; only nine townships
(18.8%) have a nodal accessibility higher than the mean.
Per capita accessibility shown in Figure 29, however, illustrates less disparity. Thirty-four of the 48 town ships' per capita accessibility fall into a plus and minus one (1) standard deviation from the mean (70.8%). Twenty out of the 4 8 townships have per capita accessibility higher than the mean, and most of those townships in the higher mean block are concentrated in the central part of the Lansing Metropolitan Area. Six townships out of the so-called "nine township area," which is composed of such townships as Watertown, Dewitt, Bath, Delta, Lansing,
Meridian, Windsor, Delhi, and Alaiedon, have per capita accessibility higher than the mean. The very low per 162
TABLE 17.— Nodal Accessibility to the Urban Functions, Per Capita Accessibility to the Urban Function, and Per Capita Income (Township Base).
Nodal ^ Per Capita Per Capita Township Population 'Accessibility Accessibility Income
Lebanon 697 89.0 0.128 3,635 Essex 1,688 149.3 0.088 3,990 Greenbush 1,929 270.0 0.140 3,381 Duplain 2,330 139.3 0.060 4,142 Dallas 2,288 134.5 0.059 3,396 Bengal 1.067 303.3 0.284 3,253 Bingham 9,747 1,167.6 0.120 4,513 Ovid 3,241 240.1 0.074 4,236 Westphalia 2,350 197.5 0.084 3,311 Riley 1,547 356.1 0.230 4,574 Olive 2,111 620.5 0.294 4,267 Victor 2,287 421.0 0.184 4,250 Eagle 2,060 445.1 0.216 4,142 Watertown 3,602 959.4 0.266 5,225 Dewitt 13,203 2,984.2 0.226 4,710 Bath 5,746 1,797.0 0.313 4,233 Sunfleld 1,998 148.6 0.074 3,627 Roxand 1,975 281.7 0.143 3,731 Oneida 10,298 1,353.7 0.131 5,091 Delta 28,262 2,484.5 0.088 5,711 Vermontville 1,942 115.8 0,060 3,660 Chester 1,622 289.3 0.178 3,958 Benton 3,907 312.8 0.080 4,194 Windso 6,078 529.0 0.087 4,862 Kalamo 1,683 115.2 0.068 3,730 Carmel 8,769 1,127.7 0.129 4,429 Eaton 4,965 304.3 0.061 4,805 Eaton Rapids 3,725 344.6 0.093 4,176 Bellevue 2,725 66.2 0.024 4,341 Walton 3,205 120.5 0.038 4,515 Brookfield 1,380 142.5 0.103 3,936 Hamlin 5,803 709.0 0.122 4,437 Lansing 116,493 16,024.8 0.138 4,998 Meridian 77,603 10,454.4 0.135 5,278 Williamscon 5,472 1,084.1 0.198 5,033 Locke 1,456 336.0 0.231 4,223 Delhi 36,722 1,005.2 0.027 4,847 Alaiedon 2,845 817.9 0.287 4,936 Wheatfield 3,004 375.4 0.125 4,937 Leroy 3,413 185.2 0.054 4,816 Aurelius 2,460 478.4 0.194 3,B22 Vevay 9,132 1,033.4 0.113 5,133 Ingham 1,974 211.5 0.107 3; 903 White Oak 1,096 119.4 0.109 4,824 Onondaga 2,289 277.5 0.121 3,372 Leslie 4,300 198.0 0.045 4,477 Bunker Hill 1,794 122.5 0.068 3,283 Stockbridge 2,914 80.4 0.028 4,186
Mean (/*> 1,073.4 0.128 4,303 Std.'-Dev. (s) 2,668.0 0.076 599
Slope 611.147 Y-interception 4223.42? Correlation! Coefficient (r) 0.078 * Value of the distant disutility parameter, K, used for this calculation is z.u 163
1 Lebanon 89.0 25 Kalamo 115.2 2 Essex 149.3 26 Carmel ,127.7 3 Greenbush 270.0 27 Eaton 304.3 4 Duplaln 139.3 *“ 28 Eaton Rapids 344.6 n r n 5 Dallas 134.5 P 29 BellavuB EZ h 66.2 6 Bengal 303.3 i I i 30 Walton 120.5 U 7 Bingham 1 .167.6 r L 3 4_ 31 Brookfield 142.5 r 8 Ovid 240.1 t 32 Hamlin 709.0 9 Westphalia 197.5 P il i 33 Lansing ,024.8 10 Riley 356.1 !_ 34 Meridian ,454.4 tz i III —1 11 Olive 620.5 t ^HBSSSSEZil j 35 Williams ton ,084.1 p 1 12 Victor 421.0| _ L L £ 36 Locke 336.0 p j □ 13 Eagle 445.1 P e e w * 37 Delhi ,005.2 P 1 H e E a r a e i E 14 Watertown 959,4 □ — 38 Alaiedon 817.9 p p 1 i_ 2 984.2 39 Wheatfleld 375.4 15 Dewitt , r 1 I 1 1 ” UP 16 Bath 1 797.0 1 40 Leroy 185.2 . p p p 17 Sunfleld 148.6 L r 11 . . _LI7ii 41 Aurelius 478.4 5 — i 18 Roxand 281.7 i | 1 42 Vevay ,033.4 i 19 Oneida 1.353.7 43 Ingham 211.5 ' “ —1 1 20 Delta 2 .484.5 z ~ 44 Whlteoak 119.4 ■ ■ ■ ■ ■ ■■■■■■ . -vaJdtoAttSMCMMfl 45 Onondaga 21 Vermontville 115.8 ■■■■■■■■■■■ Mil&RatviS'aiiiUBa 277.5 22 Chester 289.3 46 Leslie 198.0 ■RinaBHnnm .^s? tj s ~ 'zbs 23 Benton 312.8 ■■■■■■■■■■■ .as:«.tf:':.asjnBE5i 47 Bunkerhill 122.5 24 Windsor 529.0 ■■■■■■■■■■■ .S^ei^SSKEya 48 Stockbrldge 80.4 ■■■■■■■■■■■ .*-5&£Su2!S:aHlSiaiH n .I'jijsasas SiSSSESHSH :t '«&'$, *o«y» m ■ ■ as«?3»^^^8iss^5i3^sjsigS*SgBBSHg :*:$ •.-r ~ «a sh f-j aaHjgsiass^msjjs®*f, ^« a«r.:-w p' ^^«ftm sg?j«jsssft«ssssasiiw &&&&&& H5S25BBBBS ssaaaw a J 1 i | Jr M 1 ! _i z£ 1 ~j JEZ , __Zl_ _ 1 1 n A J ~~\3nEZ . — 9. I H i j Jp 3 \ jp 1 1 )L U t _Ip — p _ J 1 1 1 j 1 1 ip — , — « mi i i i i i “ i *"i [ p _j tj.i, Lu 1 ~| ~1p Zj ‘; 1 _LJ m~ 27_ 4J r "["S’n p“ j]I] < 441 I d Ea ~ p 3p □ M l Ra J ~ p r p I'M y3 m i□c a_ Z 1 1__ _j ztz LiL r t C 3 L '1 j p 1L Z j 8 j 1 J p _P r zi P ZP _iL_ 1- ___ mm _L-!zlz
[ £3,741.4 1,073.4 £1 |<3,741.4 -1,549.6 <| [<1,073.4 Mean ( ) ■ 1,073.4 Mean (/*) ■ 1,073.4 Standard Deviation (S) ■ 2,668.0
Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48)
Figure 28.— Distribution of 4 8 Townships' Nodal Accessibility. 164
1 Lebanon 0.128 25 Kalamo 0.068 2 Essex 0.088 26 Carmel 0.129 3 Greenbush 0.140 27 Eaton 0.061 4 Duplain 0.060 28 Eaton Rapids 0,093 5 Dallas 0.059 29 Bellevue 0.024 6 Bengal 0.284 hi; 30 Walton 0.038 7 Bingham 0.120 31 Brookfield 0.103 8 11 y » Ovid 0.074 • i ., s (/.«• X I 32 Hamlin 0.122 9 Wescphalla 0.084 33 Lansing 0.138 10 Riley 0.230 34 Herldlan 0.135 11 Olive 0.294 35 Williamston 0.198 12 Victor 0.184 ■ ■ ■ a n 36 Locke 0.231 13 Eagle 0.216 37 Delhi 0.027 14 Watertown -0.266 38 Alaiedon 0.287 15 Dewitt 0.226 iStfsasSSSiJ ji si .iitf S2| 39 Wheatfield 0.125 16 Path 0.313 40 Leroy 0.054 17 Sunfleld 0.074 41 Aurelius 0,194 18 Roxand 0.143 ■■■■■■ 42 Vevay 0.113 19 Oneida 0.131 ■■■■■■ 43 tngham 0.107 20 Delta 0.088 ■ M B H H wmmy#. *3 .y. a 122a a 44 Whiteoak 0.109 21 Vermontville 0.060 aft3&&33m&8& 45 Onondaga 0.121 22 Chester 0.178 sssBSSBSssissssoBSKSis 46 Leslie 0.045 23 Benton 0.08ft 47 Bunkarhlll 0.068 24 Windsor 0.0871 48 Stockbrldge 0.028 ss &($${&!& s&sm sm s * A « £* • A >.*>.. S*A aiifi$3fe$ .W 'fc»v '.•>,« rf*. ,’. _■ r-*n ar-a ** 0C. ;sf - -.•*• :■» -5 £3 .•* > s -^ 5.£! ^ r Jl TJtshj■ v?;-■* ;v a.j *4m ft* V¥ ^ £*i ZZl £2 J* x-4 < laaisssa sasna aassa m ■■^?53na mss^sssfsg&sss ^§«KBSSSS8S88S ■■KtVH ■■■■■■ "S* ■■■■■■ ^SSS^SiiS38£5:SS§ ■■■■■■ ’luSaffi ass ■■■ ■ ■ ■ ’EiSS1: ■■■■■■ ■m M N m E a H u M u . 27 ■■■■■■ ■■■■■■ ..^KSiiaaf ■■■■■■ Jiisisisa S M i M 4 J i SMSSS smm'sssss ^■SS!S$!S$S^$S
5: .204 0 . 1 2 8 ^ W B < 0.204 0.052 Mean ,( Ji ) ■ 0.128 Scandard Deviation (3) “ 0.076 Clinton County { 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33—4 8) Figure 29.— Distribution of 48 Township Nodes' Per Capita Accessibility. 165 capita accessibility at the Delhi Township node comes simply from the fact that this particular township does not have the destination node represented by a definite city size, in spite of the large population in its territory (36,722: most of those reside in the northern half of the township which is a part of Lansing). It is not an easy task to draw conclusive judgments from the above two data. Although almost half of the townships in Clinton County have higher than the mean per capita accessibility, Clinton County simply has less people than Ingham and Eaton Counties. In Eaton County, on the other hand, most of the townships (12 out of 16) have below the mean per capita accessibility. The population in Eaton County (88,337) is much more than that of Clinton County (55,893), and its population is distributed heavily toward the east side of the County, i.e., toward a highly accessible area. In fact, this part of Eaton County has experienced a very high population growth during the past decade (see Figure 23). The northwestern half of Ingham County has a higher or much higher per capita accessibility than the southeastern half. This implies that the north western part of Ingham County is less populated in comparison with the high level of nodal accessibility, especially in Alaiedon and Locke Townships, where fewer people live in comparison with their nodal accessibilities. 166 Hence, it is hard to justify Symons' assertion that highly accessible CBD areas might have lesser per capita accessibility than the nodes with lower accessibil ity but with fewer people, at least in the Lansing Metropolitan Area. A similar result was found in the relationship between per capita accessibility and per capita income. As shown in Table 17, the correlation coefficient between per capita accessibility and per capita income is very low (0.078). As Symons himself admits, and as has been shown in this part of the study, the per capita measure is not always the most appropriate way of introducing the ability of a population to acquire inherent spatial opportunities at a node or is not the most persuasive device to under stand population movement in certain regions. However, this per capita measure might be useful if it can guide the possible location or areas of residential activities in the future; for instance, by applying certain standard values of per capita accessibility, the appropriate level of the number of persons in certain areas might be designated. In fact, this per capita accessibility concept may become one of the key standards to implement necessary growth management policies to protect valuable environments, productive agricultural lands, forests, etc. 167 Turning back to the nodal accessibility of the 192 demand nodes within the Lansing Metropolitan Area, its utility value in urban and regional analyses is extremely high. Figure 30 shows the isopotential contours based on the 192 nodal accessibilities within the Lansing Metropolitan Area. The contour lines shown in Figure 30 connect points of equal accessibility. The darkest area, which is surrounded by the 1,000 isopotential contour lines, accommodates both the Cities of Lansing and East Lansing; in other words, the most urbanized area. A 500 isopotential contour line encircles the suburban areas of the above two cities. A similar nodal accessibility is seen at the nodes which represent the Cities of St. Johns, Dewitt, Grand Ledge, Charlotte, and Mason. A 300 contour line is seen in the suburban areas of the above smaller cities and in and around the City of Williamston. This isopotential contour map clearly illustrates that the Lansing urban area stretches along the east-west axis and the City of Grand Ledge is completely absorbed into the Lansing urban area. Mason, although it still maintains some urban functions as an independent destination node (along with the City of Williamston), constitutes a satellite city of Lansing. The Cities of St. Johns and Charlotte, on the other hand, maintain their status as regional core cities. It is also clearly understood that Ingham county has a strong advantage in terms of its 168 48 miles 42 St* Johns m ^20 (T 36 Dewitt Lansing East Lansing 30 Williamston Grand Ledge 50 100 24 §37 H. '•{ 142 139 1 144 /SO , 18 9 ✓ 153 V 157 A / 155 156 d 160 J 12 % 169 yd 173 174 / 172 L75 176 185 186 189 190 187 188 191 192 miles 0 6 12 18 A n 24 so 30 48 Charlotte Eaton Rapids ' 3U Hasan 300 - 500 ? 500 - 1,000 1,000 and over — Clinton County { 1-64) Lansing Metropolitan Area Eaton County ( 65-128) — Ingham County (129-192) Figure 30.— Isoaccessive Contour Lines Based on the 192 Nodes' Accessibility to Nine Urban Centers (Urban Functions). 16 9 accessibility to urban functions; then, Clinton County and Eaton County follow Ingham County, respectively. Figure 31 shows the Lansing urban area delineated by the (Lansing) Tri-County Regional Planning Commission and its relationship to what it calls the suburban centers. From Figure 3 0 and 31, one can easily distinguish a striking resemblance in terms of the shape of the "Urban Area” in Figure 31 and the area surrounded by the, 300 isopotential contour in Figure 30. This simply illustrates the reliability of the attraction-accessibility model used in this part in the study. From Figures 23 and 30, an interesting relationship between the movement of population in the Lansing Metro politan Area and the level of accessibility may be seen. In short, most of the townships which experienced a high population growth during the past decade are located between the isoaccessible contour lines of 50 and 100. However, this finding, although it is somewhat suggestive, cannot offer conclusive remarks concerning the future direction of population movements in the Lansing Metro politan Area. In fact, the (Lansing) Tri-County Regional Planning Commission estimates that most of the growth for the Lansing Metropolitan Area is expected to occur within the aforementioned "nine township area" between 1975 and 2000, which has been contrary to the "non-urban township 170 1 Lebanon 25 Kalamo 2 Essex 26 Carmel 3 Greenbush 27 Eaton 4 Duplain 28 Eaton Rapids 5 Dallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield 8 Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley 34 Meridian 11 Olive 35 Williamston 12 Victor 36 Locke 13 Eagle 37 Delhi 14 Watertown 38 Alaiedon 15 Dewitt 39 Wheatfield 16 Bath 40 Leroy 17 Sunfleld 41 Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whlteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhill 24 Windsor 48 Stockbridge Urban area ( Eg?) ) Suburban Center •••••• Suburban Corridor Setyice Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48) Figure 31.— Urban Area in the Lansing Metropolitan Area SOURCE: Tri-County Regional Planning Commission, Long Range Public Transportation Plan, Sept. 1977, p. 12. 171 growth" observed during the past decade in the Lansing 78 Metropolitan Area. So far, the attraction-accessibility indices to the existing urban functions have been investigated. It has become clear that the nodal accessibility based on the 192 nodes most reflects the state of affairs of the population distribution in the Lansing Metropolitan Area. In the following chapter, the research for an optimal site for a Lansing HSIPT system terminal will be investigated and the impact probabilities that might flow outward from that site will be examined. 7 8 The (Lansing) Tri-County Regional Planning Commission, Projected Development Patterns Year 2000, Lansing: Tri-County Regional Planning Commission, 1979, p. 15. CHAPTER V IMPACT PROBABILITIES FLOWING OUTWARD FROM THE SITE DESIGNATED FOR A LANSING HIGH-SPEED INTERCITY PASSENGER TRAIN SYSTEM TERMINAL The text of this chapter is divided into two parts. The first determines the optimal location of the site for a local HSIPT system terminal, namely within the Lansing Metropolitan area. The location of the terminal is a crucial element for the success of the overall HSIPT system. The location decision for such a crucial public facility, however, often represents a compromise between the wishes of various special interest groups and the real facts of functions and site. In order to make the location decision more objectively, the "p-Median" algorithm is introduced in the decision process. The second part examines the probable impacts flowing outward from the site designated for a Lansing HSIPT system terminal to the rest of the Lansing metropolitan area. The impacts due to the creation of the system terminal on various land use activities in the Lansing metropolitan area are also examined. 172 173 Development for the Rationale for the Site Selection for a Lansing HSIPT System Terminal The selection of the site for a HSIPT system terminal is not only the key for the success of a HSIPT system as part of a comprehensive transportation system in the future, but it is also the key to alleviating probable negative impacts on land uses and land covers due to the creation of such a system in the local areas concerned. Nevertheless, as mentioned earlier, inter-group conflicts are likely to yield a compromise over location decisions for the designation of a site for a HSIPT system terminal. To alleviate pluralistic, political, and subjective influences on location decisions, objectivity has to be a basic component of the decision process. One of the ways to realize objectivity in the process can be the adoption of location-allocation techniques. The primary objective of location-allocation techniques, as mentioned earlier, is to locate necessary public facilities most effectively for the users. It is safe to say that the introduction of objectivity will strengthen the logic and validity of the selection of a terminal site. This objectivity issue was introduced briefly in Chapter IV in terms of four prerequisites to locate optimally a HSIPT system's trackage right-of-way and 174 terminal site. To recapitulate, a HSIPT system right-of- way and terminal should: 1. not be located on productive agricultural lands, on forest lands, or on flood plains; 2. be located so as to be away from densely populated areas; 3. be located so as to be functionally linked with the existing commercial center (such as the CBD) of the area concerned; 4. be located so as to synchronize effectively with existing transportation systems such as highways, buses, mass transits, and air terminals. The first prerequisite will exclude most of the townships outside of the "nine township" from the optimal solution (see Figures 18, 19, and 20 on pages 127-129). The second prerequisite will exclude such densely populated townships as Lansing and Meridian from the optimal solution because most of their areas are covered by such urban uses as residential, commercial, and industrial. The third and fourth prerequisites will exclude all of the townships outside the "nine township" from the optimal solution (see Figure 17 on page 126). Among the four prerequisites described above, the second one is found to be the most critical. The creation of a HSIPT system terminal in densely populated areas is not only costly, but also environmentally undesirable. 175 It is costly not only because the cost of necessary land acquisition (the direct cost) in such densely populated areas is extremely high, but also because the cost of relocation of people and facilities (the displacement cost) from such areas is prohibitive. It is environmen tally undesirable because noise and vibration from a high-speed operation could seriously degrade the quality of the living environment in such areas (the social cost). As a result of these observations, seven townships in the "nine township" area (Watertown, Dewitt, Bath, Delta, Windsor, Delhi, and Alaiedon) were selected as the prime candidates for the location of a Lansing HSIPT system terminal. In other words, each one of these seven townships could be the optimal solution for a Lansing HSIPT system terminal. These seven townships, as mentioned earlier, are analyzed by the PMEDIAN program so as to locate a Lansing HSIPT system terminal more objectively. The following is a brief explanation of PMEDIAN adopted in this part of the research undertaking. The PMEDIAN program is one of twelve modules accommodated in FLOW, Version 5. The data needed to run PMEDIAN are basically the data shown in Table 18. In PMEDIAN, it is possible to select one node or a group of nodes as most desirable for a final solution. It is also possible to identify the least desirable node or nodes for a final solution. Further, it is possible to identify the 176 TABLE 18. Name of 48 Township Nodes, Their Cartesian Coordinates and Weights for the "p-Median Problem." Name of Township Cordinate . Weight* Node X Y (population) Remarks 1 Lebanon 7.5 2.5 697 includes part of Hubbardson Tillage 2 Essex 7.5 3.5 1,688 Includes Maple Rapids Village 3 Greenbush 7.5 4.5 1,929 4 Duplaln 7.5 5.5 2,330 Includes Elsie Village s Dallas 6.5 2.5 2,288 includes Fowler Village 6 Bengal 6.5 3.5 1,067 7 Bingham 6.5 4.5 9,747 Includes St. Johns City 8. Ovid 6.5 5.5 3,241 Includes Ovid Village 9 Westphalia 5.5 2.5 2,350 includes Westphalia Village 10 Riley 5.5 3.5 1,547 11 Olive 5.5 4.5 2,111 12 Victor 5.5 5.5 2.2B7 13 Eagle 4.5 2.5 2,060 14 Watertown 4.5 3.5 3,602 15 Dewitt 4.5 4.5 13,203 include DeWitt City 16 Bath 4.5 5.5 5,746 17 Sunfleld 3.5 0.5 1,998 includes Sunfleld Village 18 Roxand 3.5 1.5 1,975 includes Kulliken Village 19 Oneida 3.5 2.5 10,298 includes Grand Ledge City 20 Delta 3.5 3.5 28,262 21 Vermontville 2.5 0.5 1,942 Includes Vernontville Village 22 Chester 2.5 1.5 1,622 23 Beaton 2.5 2.5 3,907 includes Potterville City 24 Windsor 2.5 3.5 6,078 includes Dimondale Village 25 Kalamo 1.5 0.5 1,683 26 Carmel 1.5 1.5 6,268 includes a half of Charlotte City 27 Eaton 1.5 2.5 7,466 Includes a half of Charlotte City 28 Eaton Rapids 1.5 3.5 5,073 includes a half of Eaton Rapids City 29 Bellevue 0.5 0.5 2,725 includes Bellevue Village 30 Walton 0.5 1.5 3,205 Includes Olivet City 31 Brookflaid 0.5 2.5 1,380 32 Hamlin 0.5 3.5 4,455 Includes a half of Eaton Rapids City 33 Lansing 3.5 4.5 116,493 includes most of Lansing City 34 Meridian 3.5 5.5 77,603 includes East Lansing City 35 Williamston 3.5 6.5 5,472 includes a half of Williamston City 36 Locke 3.5 7.5 1,456 37 Delhi 2.5 4.5 36,722 includes part of Lansing City 38 Alaiedon 2.5 5.5 2,845 39 Wheatfield 2.5 6.5 3,004 includes a part of Williamston City 40 Leroy 2.5 7.5 3,413 includes Webberville Village 41 Aurelius 1.5 4.5 2,460 42 Vevay 1.5 5.5 9,132 includes Mason City 43 Ingham 1.5 6.5 1,974 includes Dansvllle Village 44 Whitsoak 1.5 7.5 1,096 45 Onondaga 0.5 4.5 2,289 46 Leslie 0.5 5.5 4,300 Includes Leslie City 47 Bunkarhill 0.5 6.5 1,794 48 Stockbrldge 0.5 7.5 2,914 Includes Stockbrldge Village * ; Population data are obtained from the (Lansing) Tri-County Regional Planning Commission. 1980 CENSUS RESULTS From P.L. 94-171 tape processed by the Tri-County Regional Planning Commission (rrlmeo). 177 several most feasible node or nodes for a final solution; that is, a node or nodes put into this category is not, or are not, discriminated from or subjectively put into a final solution. The selection of the node(s) in a final solution, however, does not always match with the subjective judgment described above. The three options noted, however, permit the users of the program to write several different 79 scenarios to find an optimal solution. Accordingly, in this analysis, the above seven townships were designated for the feasible solution and the remaining 41 townships were designated as undesirable nodes for the final solution. The following data were logged in PMEDIAN: Number of demand nodes 48 (4 8 townships) a Number of supply nodes 1 (1 township) * * Number of links 8 0 Weight of node Population (assumed to concentrate on the centroid of the township) AAA Distance between nodes Distance based on the city block metric system A Node where the facility will be created. A A Paths between 4 8 townships. tff Concerning the transportation problem in terms of 7 9 Concerning the detail of the PLOW program, refer Robert I. Wittick, FLOW, Version 5 - Technical Report 4 (MSU Software Reference Manual), September 1980, East Lansing: Michigan State University. 178 euclidian space, the rectangular street system will be called the street system of the "city block metric," or as some others call it a street system based on Manhattan geometry. The distance based on the city block metric is different from the distance calculated from the Pythagorean theorem. Thus, if there are two places (nodes) whose cartesian coordinates are (X^, Y^) and (X^, Y2) respec tively, the distance between these two places (nodes), based on the city block metric will be: d = [ (X^ - X2) + (Y^ ” Y2^l ' rat*ler than; d = >/(X^ - X^) ^ + (Y^ - Y2 ^ ' which is calculated from the Pythagorean theorem. In this study, the distance between the 4 8 townships within the Lansing Metropolitan Area will be figured by the city block metric system based on the aforementioned "non-urban township growth" trend and the existence of the rectangular street system. Figure 32 shows the locations of 4 8 demand nodes and the paths between those 4 8 demand nodes. The result of the p-Median algorithm was the selection of Delhi 179 Lebanon 25 Kalamo Esaex 26 Carmel Greenbush 27 Eaton 4 Duplaln E - 28 Eaton Rapids p. uanas nj r 29 SallQVue 6 Bengal 1 j 1 1 30 Walton 7 Bingham i 31 Brookfield 8 Ovid 32 Hamlin 9 Westphalia i 33 Lansing 10 Rilev : 34 Meridian c - 7 1 11 Olive 1 35 Williamston 12 Victor 36 Locke 13 Eagle 37 Delhi | lb, watertown i I 38 Alaiedon 15 Dewitt 39 Wheatfield 16 Bath ij | 40 Leroy 17 Sunfleld 7 41 Aurelius 18 Roxand 42 Vevay 43 Ingham 20 Delta ! 44 Uhlteoak £ 1 VCUBOnLVlllt 45 Onondaga 1 l is 22 Chester .2 46 Leslie 23 Benton 1 47 Bunkerhill 24 Windsor 48 Stockbrldgen j,7 , *rn i I j i i i r ,I 23 ft 7 \ TR 1 4 3i 27 i r n ?i 11 ,7 1 h \ 1 Path between Nodes • Centroid of township: the point at which population is assumed to concentrate. Clinton County { 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48) Figure 32.— Geocode System for 48 Townships in the Lansing Metropolitan Area. 180 Township as the optimal location for a Lansing HSIPT 8 0 system terminal. Although Delhi Township was already found to be the node that represents the feasible solution based on the aforementioned four prerequisites/ the following points should be considered to strengthen the rationale which can support this selection. Those are: 1. Delhi Township is the third largest township in terms of the number of its residents; however, more than 80 percent of its residents live in the northern half of the area which is incorporated in the City of Lansing. Accordingly, it is reasonable to assume that there will be no significant negative impacts on the existing communities if a HSIPT system terminal were built in the southern half of the area; 2. Delhi Township is obviously one of the most accessible areas from both of the most populated areas, the Cities of Lansing and East Lansing. To link the site for a q a A mathematical formulation of the "p-Median Problem" in the city block metric as: n m n m . minimize z = Z Z a..w, w. - y . + Z I a..w. x. - y. i=l j=l 13 11 1 31 i=l j=l 13 11 1 3 where z = the aggregate distance from all demand points to their closest supply center xi * yi = coordinates of the ith demand point Xj, yj = the coordinates of the jth supply center w = the weight assigned to the ith demand point a^^ = 1 when the demand point i is served from supply center j, otherwise a^j = 0 . Lansing HSIPT system terminal in this township with the existing commercial centers in both cities could be accomplished fairly easily; Delhi Township accommodates two major interchanges of the interstate highway network. One is the interchange of 1-96 which connects Lansing to Grand Rapids and Detroit and US-27 (partially used as U.S. 1-69) which connects Lansing to Flint and Battle Creek and the other is the interchange of 1-96 and US-127 which connects Lansing to Jackson. The existence of these two major interchanges could strengthen the potential of Delhi Township as the optimal location for a Lansing HSIPT system terminal; Delhi Township per se does not belong to the group of townships which scored a high growth during the 1970's; it is located almost in the center of those highly developed townships (see Figure 2 3 on page 141). The introduction of a Lansing HSIPT system terminal in this township will strongly enhance locational benefits for this entity as well as for those highly developed townships noted; Delhi Township has an area which could be sensitive to development and which requires a large amount of groundwater in its eastern half; however, the south western corner of the area, where a HSIPT terminal 182 could be constructed would not be restricted by such a constraint (see Figure 21 on page 130). From these observations, the research justifies the conclusion that Delhi Township is a logical selection for a Lansing HSIPT system terminal. The final location decision concerning a HSIPT system terminal in a real situation, however, has to take into account a few more crucial economic elements which are briefly mentioned in the beginning of this chapter, in addition to the number of demands and transportation costs which are taken into account in PMEDIAN. In fact, several types of costs are ordinarily involved in the development of land resources. Those are: 1 ) the actual outlays of cash and human effort required to bring new land resources into use and to qualify partly developed resources for higher uses; 2 ) the social costs associated with individual and group sacrifices; 3) the time costs which arise because of the time it takes to bring resource developments into use; and 4) the superses sion costs associated with the frequent practice of scrapping existing developments to make way for new 81 resource uses. 81Raleigh Barlowe, Land Resource Economics: The Economics of Real Property, 2nd Edition, Englewood Cliffs: Prentice-Hall, Inc., 1972, pp. 197-218. 183 The so-called "project costs" include all of the above costs: namely, the full value of the land, labor, and materials used in establishing, maintaining, and operating the project plus an allowance for any adverse 8 2 effects resulting from the project. In reality, these "project costs," along with the number of demands and transportation costs from the locations of the demands, have to be taken into account so as to make this particular project have any real meaning. As has been discussed, Delhi Township is selected as the optimal area for the location of a HSIPT system terminal. The exact site or land parcel for a HSIPT system terminal, however, has to be narrowed down to a much smaller space than a general location in a 36 square mile township. As discussed in the previous chapter, each township is divided into 4 subareas of 9 square miles. This 9 square mile subarea would be large enough to accommodate all possible facilities related to the operation of a HSIPT system and other necessary facilities such as hotels, department stores, parking ramps, retail shops, etc. which likely follow the development of such a new rail terminal. In conclusion, by taking into account various constraints, the southwestern corner of Delhi Township (subarea number 147) has been selected as the 82Ibid. , p. 210. 184 exact site for the Lansing HSIPT system terminal. For the geographical location of the designated site and the relationship with nine of the most populated areas in the Lansing Metropolitan Area, refer to Figure 33. In the following section, the probable impacts from the creation of a Lansing HSIPT system terminal on the rest of the Lansing Metropolitan Area will be inves tigated and the necessary policies and plans to alleviate possible negative ramifications will be discussed. Impact Probabilities Flowing Outward from the Designated Lansing HSIPT System Terminal Site to the Rest of the Lansing Metropolitan Area The location of a Lansing HSIPT system terminal (new destination node) along with the location of nine destination nodes are shown in Figure 34. As shown in Figure 34, the weight (attractiveness) assigned to the new destination node is 37,000, which is equivalent to the population in Delhi Township (see Table 18). It will, however, be necessary to explain why the existing population in Delhi Township was selected as attractive. It is relatively easy to estimate the impacts of the development of such a high-speed rail terminal upon the neighboring areas qualitatively if the application of a similar case is allowed, such as that of the Japanese Shin Kansen. In fact, as mentioned earlier, a massive amount of construction of buildings and facilities always 185 1 Lebanon Kalaao 2 Essex ' Carmel US-27 3 Greenbuah Eaton d Duplain Eaton Rapids 5 Dallas Bellevue 6 Bengal Walton 7 Bingham Brookfield 8 Ovid Hamlin 9 Westphalia Lansing 10 Riley it. Johns Meridian 11 Olive Williams ton 12 Victor Locke 13 Eagle Delhi Id Watertown Alaiedon 15 Dewitt WheacfieLd 16 Bath Leroy 17 Sunfield Aurelius 18 Roxand Vevay 19 Oneida Ingham 20 Delta Whiteoak 21 Vermontville Onondaga 22 Chester Leslie 23 Benton Bunkerhill 2d Windsor Lansing Scockbridge i i rn flrand Ledge ! i Ea»tu_ 1 ■ Ullliamston Lansing- 1-96 Mason Charlotte Lansing' HSIPT Terminal:! Gaton Rapids US-127 US-27 Clinton County ( 1-16) Lansing Metropolitan Area Eaton County (17-32) — Ingham County (33-48) Figure 33.— Geographical Configuration of Ten Urban Centers. 43 miles 10 13 14 11 12 15 16 42 -St. Johns (7,400) 17 18 21 22 26 29 30 19 20 23 24 27 28 31 32 ^Dewitt (3,170) 36 33 34 37 38 41 42 45 ^Lansing (116,500) Grand Ledge (6,920) East Lansing (77,600) Eaton Rapids (4,510)^ Lansing HSIPT Terminal Charlotte (8,250)1 / (37-.000) ,Mason (6,020) \Williamston (2,980) .o miles Clinton County ( 1-6 4) Lansing Metropolitan Area — Eaton County ( 65-128) Ingham County (12 9-192) Figure 34.— Locations of New Lansing HSIPT Terminal Node and the Existing Nine Destination Nodes. SOURCE: Tri-County Regional Planning Commission, 19 80 Census Results. Adjusted by Shun'ichi Hagiwara. 187 accompanies the development of such a high-speed rail terminal in Japan. Those buildings and facilities built in and around the rail terminal are, in most cases, hotel and office buildings, department and retail stores, and other facilities related to transportation such as parking ramps, bus and taxi terminals, in addition to the facilities necessary for the operation of a high-speed rail system. Further, in many large cities, the expansion or new construction of highways and subway rail networks to the new rail terminal have commonly been seen. These impacts are, however, hardly quantifiable. As discussed in the previous chapter, Delhi Town ship, the designated site of the nominated Lansing HSIPT system terminal, does not have a key "urban center" such as the Cities of Lansing and East Lansing within its territory, despite the fact that a large population resides in that township. Consequently, the township nodal accessibility and per capita accessibility, in particular, are very low, in spite of its locational superiority to the most densely populated areas of the Lansing Metropolitan Area such as the Cities of Lansing and East Lansing. The research was focused on this large population; in other words, on the potential of 37,000 residents and designated it as the weight (attractiveness) of the new destination node (the Lansing HSIPT system terminal). The underlying assumption for the selection of the existing population 188 as the weight (attractiveness) is that the new destination node can be expected to offer some levels of urban functions which should be enjoyable to those 37,000 residents. To examine the probable impacts of the creation of a HSIPT system terminal at the 147th subnode in Delhi Township, as done in the previous chapter, the attraction- accessibility indices were calculated. The formula used for this part of the research is: m Ej A. = I --- where A = accessibility at node i 1 j=l E = size of attraction at node j ^ D = distance disutility between node i and j k = an exponent describing the effect of the travel time between node i and j (k = 2 which is obtained from the previous calculation) m = number of destination (place of attraction) j = 1 , ..... 10 i = 1, ...... 192 The result of the calculation is shown in Table 19. The isopotential contour map based on the nodal acces sibility of 192 nodes is shown in Figure 35. The isopotential map shown in Figure 35 should be interpreted as follows: 1. The area surrounded by a 1,000 contour line now appears in two locations. Those are the existing Lansing urban area and the area around a new Lansing HSIPT system terminal at the 147th subnode in Delhi Township. The 189 TABLE 19.— The 192 Nodal Accessibilities to Ten Major Urban Centers (Urban Functions) in the Lansing Metropolitan Area. m C 1 SI1 1 EJ A ■H _ ^ where 1 ■ l,,..,192; j ■ 1,... ,10; k - 2.0. '•O _ k ij 1 19.8 49 69.5 97 31.4 145 801.9 2 23.8 50 103.3 98 48.5 146 357.9 3 24.0 51 101.3 99 23.9 147 3866.7 4 29.8 52 197.1 100 31.8 148 567.7 5 29.0 53 135.6 101 123.3 149 366.1 6 37.6 54 235.9 102 865.2 150 188.5 7 38.2 55 217.9 103 49.2 151 323.2 8 55.9 56 417.2 104 124.3 152 158.2 9 55.4 57 722.0 105 144.8 153 136,8 10 40.9 58 294.9 106 ' 97.3 154 118.8 11 126.8 59 1462.9 107 64.8 155 108.8 12 60.5 60 573.9 108 66.9 156 80.5 13 36.1 61 321.3 109 119.2 157 69.8 14 28.6 62 164.4 110 201.0 158 50.1 15 48.1 63 1030.4 111 86.8 159 56.7 16 36.2 64 315.1 112 155.7 160 42.8 17 30.0 65 38.8 113 19.6 161 542.7 18 39,3 66 50.4 114 24.5 162 264.1 19 33.7 67 33.5 115 16.2 163 196.9 20 43.0 68 42.5 116 19.6 164 132.0 21 56.7 69 68.9 117 32.8 165 744.0 22 127.4 70 103.4 118 50.9 166 159.1 23 55.4 71 56.2 119 24.5 167 160.2 24 80.5 72 79.2 120 32.8 168 82.7 25 799.0 73 197.2 121 46.4 169 90.0 26 134.0 74 888.7 122 57.1 170 63.4 27 164.7 75 109.1 123 33.7 171 63.7 28 90.4 76 206.1 124 39.8 172 47.6 29 71.3 77 478.1 125 103.4 173 46.4 30 46.3 78 1445.5 126 521.2 174 35.9 31 78.2 79 231.4 127 52.9 175 36.6 32 56,1 80 441.5 128 101.0 176 29.2 33 40,5 81 30.8 129 11935.6 177 153.3 52.5 93.0 34 82 39.3 130 219B.5 17 a 35 51.4 83 30.8 131 1509.5 179 81.2 36 69.7 84 41.0 132 599.5 180 62.6 37 66.8 85 52.7 133 8114.5 181 85.8 36 92.9 86 78.9 134 1030.4 182 58.3 39 90.0 87 62.0 135 1048.9 183 58.0 40 132.6 88 143.0 136 330.0 184 43.2 41 146.1 89 88.0 137 282.1 185 47.5 42 109.0 90 124.4 138 192.0 186 37.2 43 238.5 91 94.2 139 227.5 187 36.7 44 161.2 92 118.8 140 415.8 188 29.7 45 105.5 93 168.6 141 107.1 189 29.6 46 72.6 94 296.6 142 71.9 190 24.2 47 165.3 95 189.5 143 112.2 191 24.0 48 103.1 96 531.7 144 65.4 192 20.4 190 46 miles St. Johns Dewitt Lansing East Lansing Lansing HSI?T Terminal Grand Ledge lUllliamston 50 300 260 189 190 4 30 ' ■’M 36 miles kCharlotte Eaton Rapids 'Maoon 300 - 500 500 - 1,000 1,000 and over Clinton County { 1-64) Lansing Metropolitan Area — Eaton County ( 65-128) Ingham County (129-192) Figure 35.— Isoaccessive Contour Lines Based on the 192 Nodes' Accessibility to Ten Urban Centers (Urban Functions). 191 size of the Lansing urban area surrounded by a 1,000 contour line has not changed significantly, compared with the same area shown in Figure 30 on page 168. These two areas, surrounded by the 1,000 contour line, may eventually be joined to each other as a unitary urban area; 2. The City of Mason, which has maintained its status as a local core city in Figure 30, is completely absorbed into the suburban area of the Lansing urban area. This implies that the City of Mason will soon to be a dormitory town of the Lansing urban area, if a Lansing HSIPT system terminal is built at the 14 7th node in Delhi Township; 3. The Cities of St. Johns and Charlotte, on the other hand, can maintain their statuses as resional core cities, respectively, even after the creation of a Lansing HSIPT system terminal; 4. The City of Eaton Rapids will emerge as one of the key regional core cities in the Lansing Metropolitan Area after the creation of a Lansing HSIPT system terminal because of its location proximity to the new terminal; 5. The City of Grand Ledge's position as a dormitory town of the Lansing urban area will not change significantly. Similarly, the City of Williamston would not be influenced greatly by the creation of the new terminal; 192 6 . The direction of the movement of the development pattern of the Lansing urban area, which has been observed during the past decades, will shift from the direction along the east-west axis to the north- south. A new urban corridor will emerge between the City of St. Johns and the City of Eaton Rapids (Figure 36) . The conceptual image of the new urban corridor illustrated in Figure 36 is more realistically shown in Figure 37. A highly urbanized core shapes like the inverse figure of "L." This inverse "L" area accommodates parts of Dewitt and Delta Townships, the Cities of Lansing and East Lansing, the western half of Meridian Township, and the western half of Delhi Township. The new urbanized area around the new urban core accommodates most of Dewitt Township, the City of Dewitt, the City of Grand Ledge, three-quarters of Delta Township, the eastern half of Windsor Township, the eastern half of Delhi Township, the northern half of Aurelius Township, the City of Mason, and the eastern half of Meridian Township. The new suburban area will accommodate the southern half of Olive Township, the southeastern half of Watertown Township, most of Oneida Township, the western half of Windsor Township, most of Eaton Rapids Township, and most of Williamston Township. For convenience sake, the whole area which includes the most densely urbanized core area, the 193 1 Lebanon 25 Kalamo 2 Essex 26 Carmel 3 Greenbush 27 Eaton 4 Duplain 28 Eaton Rapids 5 Dallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield 8 Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley IhhiiS 34 Meridian 11 Olive 35 Williamston 12 Victor 36 Locke 13 Eagle 37 Delhi 14 Wa tertown 3B Alaicdon 15 Dewitt 39 Wheatfield 16 Bath 40 Leroy 17 Sunfield 41 Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whlteoak 21 Vermontville 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhill 24 Windsor 48 Stockbrldge ion assassin Densely Local Regional Urbanized Urban Core Core o Core Major Local Corridor Hew Major Urban Corridor Major Intra-Urban Corridor — Clinton County ( 1-16) Lansing Metropolitan Area ---- Eaton County (17-32) Ingham County (33-48) Figure 36.— Concept of the New Urban Corridor in the Lansing Metropolitan Area. 194 1 Lebanon 25 Kalamo 2 Essex 26 Carmel 3 Greenbush 27 Eaton 4 Duplaln 28 Eaton Rapids 5 Dallas 29 Bellevue 6 Bengal 30 Walton 7 Bingham 31 Brookfield B Ovid 32 Hamlin 9 Westphalia 33 Lansing 10 Riley 34 Meridian 11 Olive 35 Williamston 12 Victor Locke 13 Eagle Delhi 14 Watertown Alaiedon 15 Dewitt Wheat fie Id 16 Bath Leroy 17 Sunfield Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta 44 Whlteoak 21 Vermontville ana LnjXiD 45 Onondaga 22 Chester 46 Leslie 23 Benton 47 Bunkerhlll Gr rti 24 Windsor 48 Stockbrldge tttTS CBD WllTlEBB on rnridiejt most densely urbanized area urbanized area The Hew Greater Lansing Area ^ " - -»— « suburban1 area | ~~| rural area — ClintClinton County ( 1-16) Lansing Metropolitan Area Eaton County (17-32) — Ingham County (33-48) Figure 37.— Probable Land Uses in the Lansing Metropolitan Area due to the Development of the Lansing HSIPT Terminal. 195 urbanized core area, and the suburban area will be called the "New Greater Lansing Area." To functionalize this "New Greater Lansing Area," however, several important improvements to, or new construction of, highways will be necessary. These are: 1. the improvement of US-27 between St. Johns and Lansing; 2. the improvement of M-9 9, which now connects Eaton Rapids and Lansing; 3. the improvement of M-50 between Eaton Rapids and Charlotte; 4. the construction of a feeder highway to the site of the Lansing HSIPT system terminal from the interstate highway of 1-96. The formation of the "New Greater Lansing Area" would result in rather significant changes in land uses in the Lansing Metropolitan Area. Figure 38 illustrates the important relationships between the "New Greater Lansing Area" and four important land uses in the Lansing Metropolitan Area. As shown in Figure 38, some of the productive agricultural lands are like to be deleted and replaced by the "New Greater Lansing Area." Notable aggregations of productive agricultural lands are the agricultural lands in Bingham Township where the City of St. Johns exists; the ones in Oneida Township where the City of Grand Ledge exists; the one that lies between Eaton and Eaton Rapids Townships; and the ones located 196 1 Lebanon Kalano 2 Essex Carmel Creenbush Eaton 4 Duplain Eaton Rapids 5 Dallas mm Ballsvue 6 Bengal Walton 7 Bingham Brookfield 8 Ovid Hamlin 9 Westphalia Lansing 10 Riley Meridian 11 Olive Williamston 12 Victor Locke 13 Eagle Delhi 14 Watertown Alaiedon 15 Dewitt WheatfieId 16 Bath Leroy 17 Sunfield Aurelius 18 Roxand 42 Vevay 19 Oneida 43 Ingham 20 Delta Uhiteoak 21 Vermoncville Onondaga 22 Chester Leslie 23 Benton Bunkerhill 24 Windsor Stockbridge Productive r m w Major ■ Major Groundwater KBflSHHHMost urbanized Area Agricultural Foreat I Flood'* sensitive to i::::::: 1 Urbanized Area LandB Lands Plains Development Suburban Area i i Rural Area Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48) Figure 38.— Influences of the Development of the Lansing HSIPT Terminal on Land Uses in the Lansing Metropolitan Area. 197 in the western half of Alaiedon Township. However, as shown in Figure 38, the probable damage to productive agricultural lands is minor; that is, most of the productive agricultural lands in Clinton County are preserved and the ones in Eaton County as well. In terms of the preservation of forest lands , some care may have to be taken because two large forest lands which lie between Dewitt and Victor Townships and between Maridian and Williamston Townships are likely to be consumed by the "New Greater Lansing Area." Some of the major flood plains in Victor Township may likewise be consumed. Of all the probable negative impacts on land uses due to the creation of a Lansing HSIPT system terminal the most serious may be the one on groundwater. Most of the western half of Ingham County is categorized in an area where further development is very sensitive to the amount of groundwater. Although the "New Greater Lansing Area" will stretch into the western half of Eaton County, very careful attention must be paid the relationship between the location of developable groundwater and the direction of further development in the Lansing Metropolitan Area. From these observations, the profile of the "New Greater Lansing Area" was formulated after the creation of a HSIPT system in the Great Lakes Midwest Region was developed (Figure 39). As shown, a feeder highway is extended to the Lansing HSIPT system terminal from 1-96. 19G Lebanon Kalar.o EaBex Carmel Greenbush US-27 Eaton Duplain EatOR Rapid3 Dallas Bellevue Bengal Walton Bingham Brookfield Ovid Hamlin Westphalia Lansing Riley Johna 34 Meridian Olive 35 Williamston Victor Locke Eagle Delhi Watertown Alaiedon Dewitt Wheatfield Bath Leroy Sunfield Aurelius Roxand Vevay Oneida Ingham Delta Dewitt Whlteoak Vemontvllle Onondaga Chester Leslie Benton Bunkerhlll Windsoc Capitol Stockbridge Grand a Lansln Vllllatnstoa Landing 1-96 Charlotte Hasan a ^anslng HSIPT System Terminal Eaton Rap US-127 The HSIPT System Right-of-Way Monorail or Elewated Automated Guideway Transit System c Possible extension of Monorail or Elevated Automated Guideway Transit System Improved Existing Highway System Clinton County ( 1-16) Lansing Metropolitan Area — Eaton County (17-32) Ingham County (33-48) Figure 39.— Profile of the New Greater Lansing Area After the Creation of the HSIPT System Terminal in the Lansing Metropolitan Area. 199 New improved highway systems join Eaton Rapids and Mason to the Lansing HSIPT system terminal. The existing M-50 between Charlotte and Eaton Rapids is improved significantly to be a key local corridor. Between the Capitol City Airport and the Lansing HSIPT system- terminal, new monorail or automated elevated guideway transit systems could be installed. The branch line of this automated guideway transit system could also be extended to downtown East Lansing to attract the student body of Michigan State University which could be a significant captive market for a HSIPT system. In the not far distant future, this automated guideway transit system may be extended to the Cities of Dewitt and Eaton Rapids to intensify the functions of the newly emerged urban corridor proposed in this research. A primary objective of the research reported in this chapter was the projection of the probable impacts flowing outward from the site designated for a Lansing HSIPT system terminal upon the remaining Lansing Metro politan Area. In particular, the research focuses on the impacts upon land use changes. Ordinarily, a major goal of a projection study is to offer the necessary information of how much and where possible growth or decay is likely to occur in certain areas or regions. The research findings reported in this chapter, however, do not speak to the issue of how much, but where. As a matter of fact, the 200 strongest constraint for new development is the availability of vacant developable lands. If no more vacant, devel opable lands are available in a certain region or area, the projected increase of population or employment means that there will have to be an increase of density of population or employment activities on currently used lands; and, by the same token, the projected decrease of population or employment activities means the decrease of density of those elements. In fact, one of the assumptions made by The (Lansing) Tri-County Regional Planning Commis sion, when it dealt with the study entitled Projected Development Patterns Year 2000, is that "the influence that existing land use patterns exert on future locations 8 3 of development must remain strong." Without vacant, developable lands, any significant new development cannot be implemented. In this regard, the findings reported in this chapter demonstrate that there is, and will be, ample developable land in the vicinity of a HSIPT system terminal site and all over the Lansing Metropolitan Area. The findings successfully pinpointed the direction and location of future developments due to the creation of a Lansing HSIPT system terminal in the Lansing Metropolitan Area. 8 3 The (Lansing) Tri-County Regional Planning Commission (1979), p. 4. CHAPTER VI SUMMARY AND CONCLUSION Summary of the Research Findings This research undertaking is an examination of the probable impacts on rural-urban structural change in the Lansing metropolitan area in the State of Michigan resulting from the hypothetical creation of a HSIPT system in the Great Lakes Midwest Region. The principal HSIPT system network is assumed to link such large U.S. SMSAs as Milwaukee, Chicago, Detroit, Cleveland, and Pittsburgh, along with such middle-sized SMSAs as Grand Rapids, Lansing, Flint, Toledo, and Youngstown. This research undertaking also assumes that a HSIPT system in the Region should be extended to such Canadian CMAs as London and Toronto. In recapitulation, the objective of this research undertaking is to identify and evaluate the probable impacts of a HSIPT system which has been assumed to be scheduled for development in the Region. The impacts in three distinct areas were examined: the probable impact of a HSIPT system on the forty local communities in the Region; the probable impact of a HSIPT system as a competitor to non-transportation land uses in a specific local area where a HSIPT system terminal was assumed to be 201 202 built {the Lansing terminal area); and finally the probable impact of the terminal on the rest of the area (the Lansing metropolitan area) was analyzed. Considerable reference was placed on the impacts produced by the Japanese HSIPT system, the Shin Kansen, as well as secondary sources. Regional Impacts The research produced a number of results which varied in each area of impact. Among the forty local communities selected for the first part of the research, such local communities as Grand Rapids, Columbus, Cincinnati, Kalamazoo, and Indianapolis are likely to be influenced most significantly by the introduction of a HSIPT system in the Region. Next, London (Ontario, Canada), Lansing, Dayton, Cleveland, and Detroit are also likely to be influenced significantly by the system. Such local communities as Flint, Saginaw, Toledo, Akron, Port Huron, Jackson, and Sarnia are less likely to be influenced by the system. And such local communities as Lexington, Pittsburgh, Battle Creek, Toronto, Milwaukee, and Chicago are least likely to be influenced significantly by the system. The remaining seventeen local communities, such as Ann Arbor, Gary, Windsor (Ontario, Canada), Youngstown, Canton, Louisville, Madison, Lima, Erie, Southbend, Rockford, Hamilton (Ontario, Canada), Fort Wayne, Niagara Falls (Ontario, Canada), Peoria, Buffalo, and Davenport 203 may experience negligible impacts (if any at all) from the creation of the system in the Region. Most of the twenty communities joined together by the system are likely to be influenced significantly by the system. This system, however, is less likely to influence large communities such as Chicago, Milwaukee, Toronto (Ontario, Canada), and Pittsburgh than such medium sized communities as Grand Rapids, Columbus, Cincinnati, Lansing, Dayton, and Flint. Among these twenty communities, the communities not influenced significantly are Gary, Youngstown, and Windsor (Ontario, Canada). Instead, such communities as Saginaw, Port Huron, Jackson, Sarnia (Ontario, Canada), Lexington, and Battle Creek, which are assumed not to be directly linked into the network by the system, are likely to feel fairly strong indirect impacts. This is an interesting finding, for it implies that the system is less likely to influence such communities which are located very close to large communities. The reason why the system is less likely to influence such communities as Gary, Youngstown, and Windsor is relatively simple; namely, these three communities are already influenced strongly by such large communities as Chicago, Pittsburgh, and Detroit, respectively, and the effects of the time reduction due to the creation of a HSIPT system from these three communities to such large communities as Chicago, Pittsburgh, and Detroit are very minor. Also, the reason 204 for fairly strong impacts on such communities as Saginaw, Port Huron, Jackson, Sarnia, Lexington, and Battle Creek are that these communities are located very close to the communities where a local HSIPT system terminal is assumed to be created. Different HSIPT system corridors are equally feasible. As a matter of fact, there could be a number of alternative HSIPT system routes and configurations. Those optimal network designs will be necessary subjects for further research. Terminal Site Selection Impact The compatibility of a HSIPT system as a consumer of land with other, non-transportation land uses, was verified in the second part of the research, especially such land uses important for preserving a living environment of desirable quality as productive agricultural lands, forest lands, flood plains, and groundwaters. Namely, this part of the research processes verified that such valuable land uses as productive agricultural lands, forest lands, flood plains, and groundwaters will not be negatively influenced by a HSIPT system terminal and right-of-way if such elements are properly planned and installed. To realize this dual objective, the following four conditions were designated as the prerequisites for a HSIPT system design. Those were: 205 1. A HSIPT system right-of-way and terminal should not be placed on productive agricultural lands, on forest lands, or within flood plains; 2. A HSIPT system right-of-way and terminal should be located so as to geographically separate from densely populated areas; 3. A HSIPT system terminal should be located so as to be functionally linked with the existing commercial center of the area (nominally the "central business district") concerned; 4. A HSIPT system terminal should be located so as to be capable of being synchronized effectively with existing transportation modes such as highways, buses, mass transits, and airlines. Based on these four prerequisites, the seven townships of Watertown, Dewitt, Bath, Delta, Windsor, Delhi, and Alaiedon (all of these seven townships are located in the "nine township" area) were selected as the prime candidates for the site of a Lansing HSIPT system terminal; in other words, each one of these seven townships could be an appropriate location for the site of a Lansing HSIPT system terminal without incurring unnecessary conflicts with other non-transportation land uses. This part of the research undertaking also dealt with the location decision process for a local HSIPT system terminal. The Japanese experience clearly showed that the 206 selection of the site for a HSIPT system terminal has to be made with extreme caution. In fact/ on the local level/ the success of a HSIPT system greatly depends on the issue of where the site for the system terminal should be. Nevertheless, special interest groups often affect location decisions on this sort of public facility, and, consequently, the decision finally made frequently represents a consensus of the wishes of various groups. To alleviate such political and pluralistic location decision problems, objectivity has to be introduced into the decision process. The "p-Median" algorithm (which is packaged in the MSU software program as PMEDIAN) was found to be the most appropriate method to introduce objectivity in the location decision process for such a public facility as a local HSIPT system terminal. As mentioned above, seven townships were selected as the prime candidates for the location of the site of a Lansing HSIPT system terminal; so, these seven townships were logged in PMEDIAN as the feasible solution. The result of the "p-Median" algorithm was the selection of Delhi Township as the optimal location for a Lansing HSIPT system terminal. After reviewing the existing land uses and covers in that particular township, the location of the site for a Lansing HSIPT system terminal was determined to be in the southwestern corner subnode (no. 147). 207 The site designated has an area of 9 square miles (3 x 3 mile square), which was considered to be more than enough to accommodate the various facilities which are often developed along with, or soon after, the installation of a system terminal. The facilities usually built on the site are such facilities as hotel and office buildings, department and retail stores, other facilities related to transportation (such as parking ramps, bus and taxi terminals), and the facilities necessary for the operation of a high-speed rail system. As can be seen, most of these facilities would be related to commercial activities. The site which accommodates such commercial facilities could become one of the main attractions in the metropolitan area and could exert a significant influence on the direction and intensity of urban development. The final location decision concerning a HSIPT system terminal in an actual situation, however, has to take into account various economic elements as described in Chapter V. In addition to the transportation costs of areas included in the model and environmental constraints mentioned above, the so-called "project costs," which are composed of such investment elements as direct costs for land acquisition and development, displacement cost, and social costs have to be figured out carefully. These costs, along with the number of demand and transportation costs, have to be taken into account so as to make the 208 final location decision for a HSIPT system terminal, more acceptable and economic. Local Community Impact As a necessary step prior to engaging the third part of the research commitment, the patterns of residential settlement in the Lansing metropolitan area were investiga ted. The results of this study verified that there are strong relationships between the accessibilities to attractions such as shopping opportunities, employment opportunities, and other urban functions and services and the patterns of residential settlements in the Lansing metropolitan area. The attraction-accessibility interac tion model used for this part of the research undertaking indicated that the population in the townships in the Lansing metropolitan area decrease in proportion to the inverse-square of the distance from the attractions described above. Among these three attractions, however, the urban functions were found to be the ones most strongly related to the patterns of residential settlement in the Lansing metropolitan area. Based on the findings described above, the probable impacts of the addition of a Lansing HSIPT system terminal (on the 147th subnode in Delhi Township) on the rest of the Lansing metropolitan area were investigated. The model used for this part of the research undertaking was the 209 aforementioned, attraction-accessibility interaction model. The probable impacts found are: 1) the likely formation of a new "urban core" around the existing Cities of Lansing, East Lansing and the western half of Delhi Township; and 2) a presumed shift in the direction of urban expansion in the Lansing metropolitan area from east-west to south-north. These findings can be more clearly shown in the following summary: 1. Lansing, East Lansing, and the western half of Delhi Township would tend to coalesce into a densely urbanized core in which most of the major employment and retail centers will be accommodated; 2. St. Johns, Charlotte, and Eaton Rapids would tend to function as regional cores and would serve for local needs; 3. Dewitt, Grand Ledge, Williamston, and Mason would tend to function as local cores, through strongly influenced by the densely urbanized core of Lansing, East Lansing, and the western half of Delhi Township. 4. Among the ten urban centers, Lansing, East Lansing, St. Johns, Charlotte, Eaton Rapids, Dewitt, Grand Ledge, Williamston, Mason, and Delhi (the Lansing HSIPT system terminal site), the most drastic change due to the creation of a Lansing HSIPT system will occur in the City of Mason. Because of its locational proximity to the new terminal, Mason would be absorbed 210 in the Lansing urban area completely. Another community which is likely to have significant impacts from the new system will be Eaton Rapids. At the southern end of a newly developing urban corridor, the role of Eaton Rapids will be intensified. Some of the urban functions now located in Charlotte might shift to Eaton Rapids. The policies and plans necessary to alleviating the probable negative impacts on social, economic, physical, and environmental structures in this case would have to be developed and implemented before the loss became irretrievable. Conclusions In conclusion, this part of the research undertaking verified the utility and workability of the "p-Median" method and the attraction-accessibility interaction model. The "p-Median" method, for instance, successfully introduced objectivity into the location decision process for a HSIPT system terminal site in the Lansing metropolitan area. The attraction-accessibility interaction model predicted the shift of the direction of urban expansion in the Lansing metropolitan area due to the creation of a Lansing HSIPT system terminal. In other words, by adopting the above method and model, the probable conflicts of social, economic, physical, and environmental factors in the Lansing metropolitan area due to the creation of a Lansing 211 HSIPT system terminal could be anticipated, examined, and perhaps reduced in advance of the product. The results of this type of research can be extreme ly crucial for both public and private policy decision makers. For public decision makers, the result could be referred to for future action in land management, land use control, public investment, growth management, etc., and for private decision makers, it could help to develop future investment strategies within the area concerned. More importantly, however, the application of this research method will be important to any of the potential sites for a HSIPT system terminal and to the surrounding areas within any region where a HSIPT system is planned or expected. This research undertaking has been predicted on the assumption that a HSIPT system had been authorized for development in the Great Lakes Midwest Region. Applications of the research methods formulated and utilized in this dissertation do produce convincing results and could be important to any community in any region or country where a HSIPT system is planned or expected. The general con clusions drawn from the findings of this research undertaking can be summarized as follows: 1. A HSIPT system, as Ogawa mentioned in his study concerning the possible impacts of the Tokaido Shin Kansen on the cities along its route and also as described in this research undertaking, would be a 212 system which joins smaller communities to larger communities, rather than the reverse. This means that the probable development impacts of a HSIPT system will be stronger on smaller communities than on larger communities. Exceptions would be cases where smaller communities are located very close to extremely large communities. In this research, such communities as Gary, Youngstown, and Windsor, which are located very close to Chicago, Pittsburgh, and Detroit, respectively, are cited as the examples of the case described above; 2. The probable impacts of a HSIPT system may not always be stronger on the communities which are directly joined by the system than on the communities not joined by the system. For instance, in this research under taking, the probable impacts of a HSIPT system on such local communities as Saginaw, Lexington, Port Huron, Sarnia, and Battle Creek, which would not be joined by the system, were found to be much stronger than the ones on such local communities as Toronto, Milwaukee, Gary, Chicago, Youngstown, and Windsor which are directly joined by the system. It is important to recognize this fact so as not to overestimate or underestimate the probable impacts of a HSIPT system; 3. The direction of urban expansion will shift toward the site designated for a local HSIPT system terminal where 213 various commercial activities would presumably be located. In reality, however, this shift will occur only if the site is located so as to be functionally linked with the existing commercial center of the area (CBD) and if the site is located so as to synchronize effectively with existing transportation systems such as highways, buses, mass transit, and air. If these basic prerequisites were not met the site would be left without sufficient attractiveness for development investment dynamics and inefficient and non-effective dual investments in small local areas would result; 4. The direct impacts of a HSIPT system on existing land uses would be minor because the space necessary for a HSIPT system terminal and trackage right-of-way would be considerably smaller than that necessary for the infrastructure of other transportation systems such as highways and airports. The indirect impacts of a HSIPT system on various land uses are, however, considerably significant. In fact, the creation of such an important transportation facility as a HSIPT system terminal would generate significantly improved accessibility to certain portions of the local area; it would also result in a considerable increase of the developmental potential of the portions in the area concerned. The areas most strongly influenced by the 214 creation of a HSIPT system terminal are, most probably, the areas generally adjacent to the terminal site. The impacts on investment attractiveness decrease in proportion to the distance; as found in this dissertation analysis, impacts decrease in proportion to the inverse square of the distance from the designated terminal site. The developmental potential generated by this improved accessibility is often directed into residential types of land use activity and the result is a massive conversion of irreplaceable, agricultural and forest lands into residential land use. Careful analysis and planning and rigorous public policies and controls would be necessary to preserve such important land resources for support for living environments of desirable quality (i.e. , productive agricultural lands, forest lands, flood plains, and groundwaters). Suggestions for Further Studies As mentioned in the preceding section, this research undertaking was predicted on the assumption that a HSIPT system were to be created in the Great Lakes Midwest Region. In reality, however, the time and place for the eventual implementation of a HSIPT system in the United States are still uncertain. Moreover, whenever the issue of a HSIPT system becomes the subject of 215 discussion, there is one, very basic question that the advocates of a HSIPT system in the United States must always encounter. That is the question: "Could a HSIPT system really be successful in the United States?" It is safe to say that a clear and decisive answer to this question has not yet been developed. Typical counter arguments to the creation of a HSIPT system in the United States which HSIPT system planners in the United States 84 have to overcome are: 1. on-line population densities in Europe and Japan are much greater than in the U.S. Long-distance trains in Europe connect a series of short-distance corridors, but unlike the U.S. network, the corridors are adjacent . . . Only the Northeast Corridor has on-line population densities comparable to major West European and Japanese routes; 2. travel habits are different for Europeans and Japanese. For several decades, the overwhelming majority of Americans have relied on private transportation. The auto has largely determined our residential living patterns. Decentralization of the urban population has not only caused people to move further out into the suburban rings, but has also allowed them to disperse from the transportation spokes that radiate out from the Central Business Districts; 3. the phenomenon that we have witnessed in the U.S. is now happening in Western Europe and Japan. Rising real incomes have allowed people to desert mass transit and intercity public transport modes . . . It seems clear that the "habit" of reliance on public transportation is one that many travellers find easy to break; 84 National Transportation Policy Study Commission, AMTRAK: AN EXPERIMENT IN RAIL SERVICE, Wash., D.C.: NTPSC, August 1978, pp. 189-190. 216 4. public promotional and subsidization policies overseas have not favoured the air and highway modes at the expense of their nationalized rail systems. This is beginning to change as governments respond to their publics' demand for an improved intercity highway system; 5. the European and Japanese transport environments are characterized by: shorter travel distances between major urban centers, higher per-passenger- mile air fares, much higher gasoline prices, a less developed highway network, and a rail system which is dedicated more to passenger than to freight services . . . What is surprising is that foreign rail passenger systems are also losing riders and experiencing rising deficits . . . Foreign experience, therefore, is not greatly relevant to the evaluation of AMTRAK. Among these five, the issue of on-line population densities has so far been the strongest. In fact, it could be the hardest one which HSIPT system planners have to answer. For instance, more than 48 million people live along the lines of the two existing Shin Kansens, Tokaido between Tokyo and Osaka, and Sanyo between Osaka and Fukuoka. The combined total length of these two lines is approximately 670 miles (Per mile population is 71,600 persons). The Great Lakes Midwest Region, where a HSIPT system is being assumed in this research undertaking as being scheduled for installation, has approximately 40 million people (based on the population of the forty local communities selected for this research). However, the total length of the HSIPT system planned within the Region is 1,560 miles (based on the highway mileage), which is more than twice as long as the Shin Kansen routes in Japan. 217 Thus, the per mile population in the Great Lakes' case is 25,600/ which is equivalent to approximately 36 percent of the on-line population in the Japanese case. This comparison, however, ignores the fact that the development of the Shin Kansen in Japan has spurred the influx of population into the areas where the service of the Shin Kansen could have been expected or already existed. For instance, in 1960, four years before the Tokaido Shin Kansen between Tokyo and Osaka was in operation, the population on the Shin Kansen route between Tokyo and Fukuoka was 28 million (41,800 persons per mile). During the past two decades, the population along the Shin Kansen has increased by more than 70 percent. Obviously, this 41,8 00 per mile population is still larger than the Great Lakes Region's 25,600 per mile population, but the difference was reduced considerably. This may suggest that the probable impacts of a HSIPT system on population movement may also have to be taken into account as an important aspect of the system if it were planned to be created in any region or in any country. Needless to say, the circumstances in the United States require more careful analysis than those in other nations where existing geographic, demographic, socio economic, political, and even cultural systems are better suited to utilizing optimally such a transportation system as the HSIPT. Nevertheless, it could be safe to say that 218 the necessity for undertaking rigorous studies concerning the possible development of a HSIPT system in the United States has significantly increased. For instance, the aforementioned U.S. National Governors' Association expressed strong support for the development of a national rail passenger system that emphasizes high-density corridors: As our entire transportation system faces growing demand, deteriorating infrastructure and tighter fiscal constraints, the need for utilizing each mode to its maximum capacity is apparent. . . . Over the last few years a growing number of states have recognized the need for rail passenger transportation as an integral part of a balanced transportation system. Rail passenger service provides an alternative, especially for medium-sized cities in highly populated corridors of less than 50 0 miles. With today's average air flight over 1000 miles and the average bus trip under 100 miles, passenger rail is ideally suited to provide service to medium-distance markets along corridors throughout the country and it can be developed in a manner to provide integrated travel service with the air and bus modes. To make a HSIPT system an integral part of a balanced national transportation system, continued research in several aspects of high-speed train systems has to be undertaken by system planners in the United States. First, it is necessary to develop transportation demand models which are sufficient to be general; in other words, models have to be developed which can forecast the effects changes, such as increased speed, safety and frequency of □5 The U.S. NGA Committee on Transportation, Commerce, and Technology (1981), p. 1. 219 services on passenger demand. Concerning the limitation 8 6 of existing demand models, Lave says: Transportation demand models tend to be fragile and limited. They are developed for specific purposes and are unreliable when used more generally. In particular, past models have been deficient in not explaining passenger demand in terms of the attributes of the modes. This means that there is no way to forecast the effect of changes such as increased speed or safety on passenger demand. Furthermore, these models are invariably mode specific and consequently provide no way of looking at total travel time when one mode is changed significantly or a new mode introduced. [underlining, the writer.] Second, once market potentials were established for a HSIPT system in any region in the United States, the optimal HSIPT system network design will be necessary. As mentioned in the Japanese Shin Kansen's case, the disparity in terms of the level of economic development between the communities which have had the services of the Shin Kansen and the communities which have not had such services has been conspicuous. Third, along with analyses concerning optimal HSIPT system networks, studies concerning desirable systems of operation will be necessary. As a specific example, it is necessary to make a thorough feasibility study concerning an exclusive passenger operation or a passenger- freight joint operation. As mentioned earlier, the French and Japanese HSIPT installations which have exclusive and grade—separated rights-of-way have secured safety and 86 Lester Lave, "The Demand for Intercity Passenger Transportation," Journal of Regional Science, Vol. 12, No. 1, 1972, p. 71. 220 speed; however, they have proven to be an extremely costly way to develop a HSIPT system. The British system, which was designed to utilize the existing tracks with freight traffic, on the other hand, was found to be superior in terms of the cost of development. However, the number of grade crossings and severe curves on the existing tracks in the United States would jeopardize the safety of high speed operations. Not only the track system design but also the optimal management system for a joint passenger- freight operation should be included in this particular research category. Finally, intensive study concerning socio-economic impacts upon development within the principal HSIPT system regional corridors, at both the regional and local levels will be necessary. As a matter of fact, the probable impacts from the creation of a HSIPT system could be multifaceted. To make a HSIPT system project in the United States have any real meaning, final implementation decisions should be based on the results of the kinds of investigations research recommended above and on rigorous cost-benefit analyses which can assure the economic feasibility of any project. One clear perception about the state-of-the art of HSIPT systems gained from this study is that there is a great paucity of solid, reliable information available; it is a pioneer field for further research— hardly scratched open; indeed, intensive and extensive research needs to be 221 conducted in many areas of this technology. Technological data are substantially ahead of economic, social and behavioral, and political information. It is hoped that this dissertation endeavor, even though limited to a modest scope, can evoke interest among future research scholars, public and quasi-public agencies, and private transportation interests to build up a body of useful knowledge on this highly crucial mode of transportation. APPENDICES APPENDIX I.— Statistical Relationship between the Population Energy of 46 Prefectures and their 22 Socio-Economic Variables in 1968 (Correlation Coefficient). G 1.00 1.00 1.00 1.00 1.00 b 0.25 0.50 0.75 1.00 1.75 Total Prefectural Population 0.9862 0.9576 0.9297 0.9082 0.8701 Total DID Population 0.9622 0.9529 0.9400 0.9304 0.9151 Total Population of note than 100,000 cities 0.9687 0.9644 0.9552 0.9481 0.9370 Population la the Tertiary Industry 0.9772 0.9553 0.9493 0.9361 0.9096 Prefectural Per Capita Income 0.9774 0.9653 0.9493 0.9361 0.9096 Amount of Retail Sales 0.8923 0.8874 0.8746 0.8625 0.8352 Humber of Retail Stores 0.9869 0.9637 0.9373 0.9148 0.8691 Number of Enterprises 0.9800 0.9618 0.9390 0.9191 0.8762 Autoovnerahip 0.9781 0.9612 0.9383 0.9166 0.8632 Average Salary of Labors 0.7363 0.7233 0.7126 0.7079 0.7119 Number of Banks 0.9343 0.9253 0.9105 0.8973 0.8685 Number of Rail Passengers 0.8946 0.9007 0.8993 0.8978 0.8946 Amount of Industrial Output 0.9463 0.9571 0.9553 0.9475 0.9277 Number of Plants and Factories 0.9126 0.9203 0.9117 0.8961 0.8374 Amount of Cbnaumed'Electrlclty 0.9041 0.8985 0.8839 0.8684 0.8320 Number of Hevspaper Subscription 0.9721 0.9641 0.9512 0.9404 0.9180 Amount of Book Sold 0.9714 0.9564 0.9380 0.9230 0.8925 Number of Malls Delivered 0.B832 0.8718 0.B514 0.8464 0.8264 Amount of Water Supplied 0.9576 0.9602 0.9555 0.9511 0.9441 Number of Physicians 0.9624 0.9414 0.9184 0.9006 0.B713 Number of Telephones 0.9465 0.9483 0.9416 0.9341 0.9127 Amount Gas Supplied 0.9214 0.9301 0.9295 0.9277 0.9235 Source: Etsuo Yamamura and Hlroyukl Maki (1973), p. 165. APPENDIX Ila.— Time-Distance Matrix between 40 Origins and 20 Destinations by Automobile. Unit: Hour MIL CHI KAL CRD LAN FLN DET TLD IND CIN DTK COL AKR CLV TNG PIT WIN LON TRO CRY Madison 1.6 2.8 5.4 6.3 6.8 7.9 8.3 7.7 6.5 8.7 9.9 10.0 10.2 9.B 11.1 12.4 8.5 10.5 13.1 3.9 Miiuauk 0.5 1.8 4.4 4.7 5.8 6.8 7.2 6.6 5.4 7.7 8.8 9.4 9.1 8.7 10.1 11.3 7.4 9.5 12.2 2.4 Chicago 1.8 0.5 2.6 3.5 4.1 5.1 5.5 4.7 3.7 5.9 7.1 7.2 6.2 7.0 8.3 9.6 5.8 7.7 10.3 1.0 Kalamaz A.4 2.6 0.5 1.1 1.5 2.6 2.9 2.8 4.7 6.2 5.1 5.5 5.4 5.1 6.4 7.7 3,0 5.4 7.8 1.9 CrandRa 4.7 3.5 1.1 0.5 1.4 2.2 3.0 3.6 5.7 7.3 6.1 6.3 6.1 5.9 7.2 8.4 3.1 5.5 7.9 2.9 BattleC 4.8 3.1 0,5 1.4 1.0 2.1 2.3 2.3 4.2 4.9 4.5 5.0 4.8 4.6 5,9 7.1 2.4 4.8 7.2 2.4 Lansing 6.8 4.1 1.5 1.4 0.5 1.1 1.7 2.3 5.2 5.9 5.5 5.0 4.7 4.7 5.8 7.1 1.9 4.2 6.6 3.4 Jackson 5.6 3.8 1.3 2.2 0.8 1.8 1.5 1.7 6.0 5.8 4.7 4.6 4.2 4.1 5.5 6.7 1.6 4.0 6.4 3.2 AnnAtbo 6.3 4.5 2.0 2.7 1.3 1.1 0.8 1.0 5.5 5.2 4.0 3.9 3.4 3.4 4.8 6.0 0.9 3.3 5.7 3.9 Flint 6.8 5.1 2.6 2.2 1.1 0.5 1.2 2.1 6.2 6.3 5.2 4.8 4.6 4.3 5.7 6.9 1.3 2.8 5.2 4.5 Saginaw 7.5 5.8 3.0 2.3 1.5 0.8 1.9 2.8 6.9 7.0 6.0 5.5 5.3 5.1 6.4 7.7 2.1 3.5 5.9 5.2 PottHur 8.2 6.5 4.0 3.6 2.5 1.4 1.2 2.4 6.9 6.6 5.4 5.1 4.8 4.6 5.9 7.2 1.3 1.4 3.8 5.8 Detroit 7.2 5.5 2.9 3.0 1.7 1.2 0.5 1.2 5.7 5.4 4.3 3.9 3.7 3.6 4.8 6.1 0,2 2.5 4.9 4.7 Toledo 6.6 4.7 2.8 3.6 2.3 2.1 1.2 0.5 4.5 4.2 3.1 2.7 2.5 2.3 3.5 4.7 1.4 3.7 6.1 4.3 Southbe 3.5 1.8 1.4 2.5 3.3 4.3 4.5 3.1 2.8 4.7 4.2 4.7 5.6 5.5 6.5 7.7 4.7 7.0 9.4 1.2 FortWay 5.1 3.4 2.2 3.3 2.8 3.8 3.3 2.1 2.5 3.1 2.7 3.1 4.3 4.3 5.3 6.7 3.4 5.8 8.2 2.7 Lima 6.4 4.6 3.5 4.6 4.0 3.7 2.9 1.7 3.5 2.6 1.5 1.8 3.1 3.1 4.1 5.4 3.0 5.4 7.8 3.9 Indiana 5.4 3.7 4.7 5.7 5.2 6.2 5.7 4.5 0.5 2.2 2.3 3.6 6.1 6.4 7.4 7.3 3.9 8.2 10.6 3.1 Clnclnn 7.7 5.9 6.2 7.3 5.9 6.3 5.4 4.2 2.2 0.5 1.2 2.2 4.7 5.0 5.6 5.9 5.6 7.9 10.3 5.3 Dayton 6.8 7.1 5.1 6.1 5.5 5.2 4.3 3.1 2.3 1.2 0.5 1.5 4.0 4.3 5.0 4.9 4.5 6.8 9.2 5.4 Columbu 9.4 7.2 5.5 6.3 5.0 4.8 3.9 2.7 3.6 2.2 1.5 0.5 2.6 2.8 3.4 3.8 4.1 6.4 8.8 6.0 Canton 9.5 7.8 5.B 6.5 5.1 5.0 4.1 2.9 6.0 4.7 3.9 2.5 0.5 1.2 1.0 2.0 4,3 6.6 6.9 7.2 Akron 9.1 7.4 5.4 6.1 4.7 4.6 3.7 2.5 6.1 4.7 4.0 2.6 0.5 0.8 1.0 2.4 3.9 6.2 6.9 6.7 Clevela 8.7 7.0 5.1 5.9 4.5 4.3 3.5 2.3 6.4 5.0 4.3 2.8 0.8 0.5 ■ 1.4 2.6 3.6 6.0 6.0 6.5 (continued) Appendix Ila.— Continued. Unit: Hour MIL CHI KAL GRD LAN FLN DET TLD IND CIN DTN COL AKR CLV YNG PIT WIN LON TR0 GRY Youngst 10.1 8.3 6.4 7.2 5.8 5.7 4.8 3.5 7.4 5.6 5.0 3.4 1.0 1.4 0.5 1.4 5.0 6.9 5.9 7.7 Fittsbu 11.3 9.6 7.7 8.4 7.1 6.9 6.1 4.7 7.3 5.9 4.9 3.B 2.4 2.6 1.4 0.5 6.2 7.5 6.5 8.9 Windsor 7.4 5.8 3.0 3.1 1.9 1.3 0.2 1.4 5.9 5.6 4.5 4.1 3.9 3.6 5.0 6.2 0.5 3.4 4,2 4.8 London 9.5 7.7 5.4 5.5 4.2 2.8 2.5 3.7 8.2 7.9 6.8 6.4 6.2 6.0 6.9 7.5 2.4 0.5 2.4 7.2 Toronto 12.2 10.3 7.7 7.9 6.6 5.2 4.9 6.1 10.6 10.3 9.2 8.8 6.9 6.0 5.9 6.5 4.8 2.4 0.5 9.6 Buffalo 12.6 10.8 8.5 8.6 7.3 5,9 5.6 6.2 10.2 8.8 8.1 6.7 4.8 3.9 3.8 4.4 5.5 3.1 2.1 10.3 Sarnia 8.3 6.5 4.0 3.6 2.5 1,5 1.2 2.4 4.6 6.6 5.5 5.1 5,3 4.6 6.0 7.2 1.3 1.4 3.7 5.8 Ha mil to 11.1 9.3 7.0 7.1 5.8 4.4 4.1 5.3 9.8 9.5 8.4 8.0 6.0 5.1 5.0 5.6 4.0 1.6 0.9 8.8 Niagara 12.5 10.7 8.1 8.2 6.9 5.5 5.2 o.4 10.6 9.2 8.5 7.1 5.7 4.3 4.2 4.8 5.1 2.7 1.6 9.9 Gary 2.4 1.0 1.9 2.9 3.4 4.5 4.7 4.3 3.1 5.3 5.4 6.0 6.7 6.5 7.7 8.9 4.9 7.2 9.6 0.5 Rockfor 1.9 1.8 4.3 5.4 6.7 7.7 7.3 6.6 5.5 7.7 8.8 9.0 9.1 8.0 1C.1 11.3 7.4 9.5 12.1 2.4 Peoria 4.5 3.2 5.9 6.7 8.1 9.1 8.7 8.0 4.3 6.5 6.6 7.9 10.4 10.1 11.5 11.6 8.6 10.9 13.5 3.8 Davenpo 4.1 3.4 6.0 7.0 8.3 9.3 8.9 8.3 6.3 8.5 8.6 9.8 10.8 10.4 11.7 11.6 9.1 11.1 13.7 4.0 Loulsvi 7.7 6.0 7.3 8.3 9.1 8.6 7.5 6.3 2.5 2.1 3.2 4.6 7,1 7.4 8.0 8.3 7.6 10.0 12.3 5.4 Erie 10.7 9.0 7.1 8.5 7.1 6.6 5.5 4.3 9.1 7.7 7.0 5.5 3.0 2.0 1.9 2.6 5.6 5.6 5.0 8.5 Lexingt 9.2 7.5 8.8 9.8 9.2 8.1 7.0 5.8 3.8 1.6 2.7 3.8 6.3 6.6 7.2 7.5 7.1 9.5 11.9 6.9 Note: The time-distance between each node (SMSAs or CMAs) is calculated as follows: Time-distance (hour) ■ the highway mileage between nodes (miles)/ aasuned average speed of automobile (50 mph) For instance, the time-distance between Youngstown and Milwaukee - 501/50 - 10.02 -- 10.1 (hours) APPENDIX lib.— Time-Distance Matrix between 40 Origins and 20 Destinations by the HSIPT System. Unit: Hour MIL CHI KAL GRD LAN FLN DET TLD IND CIN DTK COL AKR CLV TOG PIT WIN LON TRO GRY Madison 1.5 2.7 3.7 4.1 4.6 7.9 8.3 7.7 4.1 8.7 9.9 10.0 10.2 9.8 11.1 12.4 8.5 10.5 13.1 3.0 Milvauk 0.5 1.8 2.2 2.6 3.1 3.5 3.9 4.4 2.6 3.5 3.9 4.4 9.1 8.7 10.1 11.3 4.5 9.5 12.2 1.4 Chicago 1.6 0.5 1.5 1.9 2.4 2.8 3.2 3.7 2.0 2.8 3.2 3.8 6.2 4.6 8.3 9.6 3.8 7.7 10.3 1.0 K&lamaz 2.2 1.5 0.5 1.1 1.5 1.8 2.3 2.7 2.9 3.8 4.2 3.8 3.9 3.6 4.3 7.7 2.8 3.7 4.6 1.9 GrandRa 2.6 1.9 1.1 0.5 1.4 1.4 1.9 2.3 3.3 4.1 3.9 3.4 3.5 3.2 3.9 4.4 2.4 3.3 4.2 1.7 BattleC 2.7 2.0 0.5 1.4 1.0 2.1 2.4 2.8 3.4 4.3 4.4 3.9 4.0 3.7 4.3 7.1 2.9 3.8 7.2 1.8 Lansing 3.1 2.4 1.5 1.4 0.5 1.1 1.7 1.8 3.8 3.8 3.4 2.9 3.0 2.7 3.3 3.9 1.9 2.8 3.7 2.2 Jackson 3.4 2.8 1.3 1.8 0.8 1.8 1.5 1.7 4.2 4.2 3.8 3.2 3.3 3.0 3.7 4.2 1.6 3.0 3.9 2.5 AnnArbo 4.1 3.4 2.0 2.3 1.3 1.1 0.8 1.0 4.4 3.5 3.1 2.5 2.6 2.3 3.0 3.5 0.9 2.3 3.2 3.1 Flint 3.5 2.8 1.8 1.4 l.l 0.5 1.2 1.4 4.3 3.4 3.0 2.5 2.6 2.3 3.0 3.5 1.3 2.7 3.6 2.6 Saginaw 4.2 3.6 2.6 2.2 1.5 0.8 1.9 2.2 6.9 4.2 3.8 3.2 3.3 3.0 3.7 4.2 2.1 2.7 3.6 3.3 PortHur 8.2 4.2 3.2 2.8 2.3 1.4 1.2 2.1 6.9 4.1 3.7 3.1 3.3 3.0 3.6 4.1 1.3 1.4 2.8 4.0 Detroit 3.9 3.2 2.3 1.9 1.7 1.2 0.5 1.2 3.9 3.0 2.6 2.0 2.1 1.9 2.5 3.0 0.2 1.6 2.5 3.0 Toledo 4.4 3.7 2.7 2.3 1.8 1.4 1.2 0.5 3.4 2.6 2.1 1.6 1.7 1.4 2.1 2.6 1.4 2.5 3.4 3.5 Southbe 2.6 1.8 1.4 2.3 2.8 3.2 3.6 4.1 3.3 4.2 4.6 4.7 5.6 5.5 6.5 7.7 4.2 7.0 9.4 1.2 FortWay 4.1 3.4 3.9 3.2 3.7 3.5 3.1 2.1 2.5 4,6 4.2 3.6 3.8 3.5 4.1 6.7 3.6 4.5 8.2 3.5 Lima 6.4 4.6 4.4 4.0 3.5 3.1 2.6 1.7 3.2 2.4 1.5 1.8 3.3 3.0 3.7 4.2 3.2 4.1 7.8 3.9 Indiana 2.6 2.0 2.9 3.3 3.8 4.3 3.9 3.4 0.5 1.4 1.8 2.4 4.5 4.3 7.4 7.3 4.4 8.2 10.6 2.2 Cincinn 3.5 2.8 3.8 4.1 3.8 3.4 3.0 2.6 1.4 0.5 1.2 1.5 3.7 3.4 4.1 4.6 3.6 4.9 10.3 3.0 Dayton 3.9 3.2 4.2 3.9 3.4 3.0 2.6 2.2 1.8 1.2 0.5 1.5 3.3 3.0 3.7 4.2 3.1 4.0 9.2 3.5 Columbu 4.4 3.8 3.8 3.4 2.9 2.5 2.0 1.6 2.4 1.5 1.5 0.5 2.7 2.4 3.1 3.6 2.6 3.5 4.4 4.0 Canton 9.5 7.8 4.2 3.9 3.4 3.0 2.6 2.1 6.0 4.1 3.7 3.2 0.5 1.2 1.0 2.0 3.1 4.1 6.9 7.2 Akron 9.1 7.4 3.9 3.5 3.0 2.6 2.1 1.7 4.5 3.7 3.3 2.8 0.5 0.8 1.0 1.4 2.7 3.6 4.5 6.7 Clevela 8.7 4.6 3.6 3.2 2.7 2.3 1.9 1.4 4.3 3.4 3,0 2.4 0.8 0.5 1.4 1.7 2.4 3,3 4.2 4.3 (continued) Appendix lib.— Continued. Unit: Hour CHI KAL CRD LAM FLU DET TLO I HD CIH DTN COL AKR CLV YHG prr HIM LON TRO CRY Youngst 10.1 8.3 4.3 3.9 3.3 3.0 2.5 2.1 7.4 4.1 3.7 3.1 1.0 1.4 0.5 1.4 3.1 4.0 5.9 7.7 Plttsbu 11:3 9.6 7.7 4.4 3.9 3.5 3.0 2.6 7.3 4.6 4.2 3.6 1.4 1.7 1.4 0.5 3.6 4.5 6.5 8.9 Windsor A.5 3.8 2.8 2.4 1.9 1.3 0.2 1.4 4.4 3.6 3.1 2.6 2.7 2.4 3.1 3.6 0.5 1.4 2.4 3.5 London 9.5 7.7 3.7 3.3 2.8 2.4 2.0 2.5 8.2 4.5 4.0 3.5 3.6 3.3 4.0 4.5 1.4 0,5 1.4 4.5 Toronto 12.2 10.3 4.6 4.2 3.7 3.6 2.5 3.4 10.6 10.3 9.2 4.4 4.5 4.2 5.9 6.5 2.4 1.4 0.5 9.6 Buffalo 12*6 10.8 8.5 8.6 7.3 5.9 5.6 6.2 10.2 8.8 8.1 6.7 4.6 3.9 3.8 4.4 4.4 3.5 2.1 10.3 Sarnia 8.3 4.2 3.3 2.9 2.4 1.5 1.2 2.1 4.6 4.2 3.7 3.2 3.3 3.0 3.7 4.2 1.3 1.4 2.8 4.0 Hamllto 11.1 9.3 7.0 7.1 4.4 4.0 3.6 4.0 9.8 9.5 8.4 8.0 6.0 5.1 5.0 5.6 3.0 1.6 0.9 8.8 Niagara 12.5 10.7 8.1 8.2 6.9 5.5 4.5 6.4 10.6 9.2 8.5 7.1 5.7 4.3 4.2 4.8 3.9 3.0 1.6 9.9 Cary 1.4 1.0 1.9 1.7 2.2 2.6 3.0 3.5 2.2 3.0 3.5 4.0 4.6 4.3 7.7 8.9 3.5 4.4 7.6 0.5 Rockfor 1.9 1.8 3.3 3.7 4.2 4.6 7.3 6.6 3.7 4.6 8.8 9.0 9.1 8.0 10.1 11.3 7.4 9.5 12.1 2.5 Peoria 4.3 3.2 4.6 6.7 8.1 9.1 8.7 8.0 4.3 6.5 6.6 7,9 10.4 10.1 11.5 11.6 8.8 10.9 13.5 3.9 Davenpo 4.6 3.4 6.0 7.0 8.3 9.3 8.9 8.3 6.3 8.5 8.6 9.a 10.8 10.4 11.7 11.6 9.1 11.1 13.7 4.2 Louisvl 7.7 4.2 7.3 8.3 9.1 8.6 7.5 4.6 3.4 2.1 3.0 3.5 7.1 7.4 3.0 8.3 7.6 10.0 12.3 4.5 Erie 10.7 9.0 7.1 8.5 7.1 4.3 3.8 3.4 9.1 7.7 7.0 5.5 2.8 2.0 1.9 2.9 4.4 5.6 5.0 8.5 Lexlngt 9.2 4.4 8.8 9.8 9.2 8.1 4.6 4.1 2.9 1.6 2.5 3.1 6.3 6.6 7.2 7.5 7.1 9.5 11.9 4.4 Note: The time-distance between each node (SMSAs or CMAa) by the HSIPT system Is calculated as follows: For instance, the time-distance between Chicago and Flint. In ternal traffic friction (0.5 hours) - Internal traffic friction, i.e., time to get to the terminal (0.5 hours) + highway diatance/110 oph + highway distance/ Average speed of the HSIPT system (252/110 ■ 2.3 hours) “ 2.8 hours. A6 APPENDIX III.— Population Potential at each SMSA Based on the Time-Distance by Automobile. Unit: Person/Hour Population SMSA (1 .000s) E - 0.5 E - 1.0 E - 1.5 E - 2.0 1 Madison 290 12,199,84 5,720.24 2,999.85 1,729.71 2 Milwaukee 1,404 14,631.69 9,448.72 7,934.95 8,212.20 3 Chicago 6,975 19,561.26 18,641.16 22,321.20 29,562.93 4 Roekford 272 13,650.09 7,362.77 4,487.00 2,976.87 5 Peoria 342 11,677.76 4,999.39 2,277.97 1,094.26 6 Davenport 363 11,291.92 4,671.07 2,059.18 959.17 7 Gary 633 17,321.86 12,578.97 10,853.21 10,560.84 8 Southbend 280 15,606.17 9,060.15 5,668.86 3,779.05 9 Fort Wayne 362 14,991.04 7,914.16 4,273.62 2,352.56 10 Indianapolis 1,111 14,362.60 8,300.64 6,151.31 5,993.42 11 Louisville 867 11,727.83 4,980.60 2,260.09 1,104.63 12 Lexington 267 11,632.03 4,978.43 2,345.48 1,239.43 13 Cincinnati 1,367 14,271.04 8,605.31 6,947.79 7,301.01 14 Dayton 853 14,466.05 8.506.57 6,307.65 5,917.93 15 Columbus 1,018 14,733,74 8,705.00 6,434.21 6,081.60 16 Lima 210 15,162.18 8,232.35 4,674.93 2,777.78 17 Toledo 763 17,186.89 11,490.12 8,908.41 8,021.05 18 Cleveland 2,064 16,042.06 11,062.94 9,952.59 11,061.63 19 Akron 679 15,573.03 10,083.03 8,144.11 7,942.11 20 Canton 394 15,046.12 9,193.22 6,870.39 6,229.02 21 Youngstown 537 14,661.97 8,829.90 6,529.06 5,809.05 22 Pittsburgh 2,401 14,357.22 9,618.29 9,097.90 10,821.42 23 Erie 264 12,629.63 5,897.59 2,985.92 1,633.43 24 Buffaro 1,349 11,904.49 5,243.38 2,504.12 1,294.20 25 Detroit 4,435 18,604.82 16,165.12 18,772.90 26,268.66 26 Port Huron 36 15,127.32 8,953.05 6,067.88 4,595.85 27 Saginaw 220 14,209.02 7,622.92 4,589.38 3,119.57 28 Flint 509 15,986.93 10,173.66 7,667.59 6 j 755.78 29 Ann Arbor 234 17,525.13 12,456.74 10,440.74 9,886.56 30 Jackson 259 15,966.09 9,537.96 6,254.45 4,432.91 31 Lansing 424 15,643.63 9,362.15 6,436.87 5,182.50 32 Battle Creek 142 15,731.69 9,126.21 5,826.65 4,227.40 33 Grand Rapids 539 14,506.98 8,023.00 5,232.96 4,259.82 34 Kalamazoo 258 15,433.77 8,B36.02 5,591.58 4,036.39 3S Windsor 259 21,860.34 28,462.52 53,399.96 113,704.52 36 Sarnia 79 15,202.74 9,008.22 6,090.69 4,594.16 37 London 286 13,334.02 6,772.24 3,993.70 2,882.59 38 Toronto 2,628 13,446.18 8,932.05 8,667.67 11,091.74 39 Hamilton 499 13,193.02 7,181.33 4,902.69 4,069.60 40 Niagara Falls 303 12,111.61 5,577.51 2,897.18 1,697.90 Correlation Coefficient 0.358 0.413 0.333 0.134 A7 APPENDIX IV.— Population Potential at each SMSA Based on the Time—Distance by the HSIPT System (b = 1.0) Unit: Person/Hour Population SMSA (1,000b) b - 1.0 1 Madison 290 6,367.98 2 Milwaukee 1,404 11,484.24 3 Chicago 6,975 20,938.79 4 Rockford 272 7,794.47 5 Peoria 342 5,049.24 6 Davenport 363 4,647.72 7 Gary 633 15,212.20 a Southbend 280 9,652.98 9 Fort Wayne 362 8,194.51 10 Indianapolis 1,111 12,505.88 11 Louisville 867 5,672.18 12 Lexington 267 6,456.93 13 Cincinnati 1,387 12,831.07 14 Dayton 853 12,045.11 IS Columbus 1,018 13,163.72 16 Lima 210 9,043.45 17 Toledo 763 15,477.33 18 Cleveland 2.064 15,112.94 19 Akron 679 13,226.85 20 Canton 394 10,836.76 21 Youngstown 537 10,571.39 22 Pittsburgh 2,401 11,908.94 23 Erie 264 6,447.23 24 Buffalo 1,349 5,265.22 25 Detroit 4,435 20,570.82 26 Port Huron 36 11,293.19 27 Saginaw 220 10,410.39 28 Flint 509 14,355.34 29 Ann Arbor 234 15,145.94 30 Jackson 259 12,035.18 31 Lansing 424 13,149.42 32 Battle Creek 142 11,606.64 33 Grand Rapids 539 13,B61.91 34 Kalamazoo 258 13,729.19 35 Windsor 259 31,577.54 36 Sarnia 79 11,287.27 37 London 286 10,172.53 38 Toronto 2,628 10,810.08 39 Hamilton 499 7,534.73 40 Niagara Falls 303 5,771.46 A8 APPENDIX V.— Population Energy (Energy of Interchange) at each SMSA Based on the Time-Distance by Automobile. Unit: Square-Person/Hour SMSA b - 0.5 b - 1.0 b - 1.5 b - 2.0 1 Madison 290 3,537,954.9 1,658,870.1 869,957.9 501,616.2 2 Milwaukee 1,404 17,755,178.4 9,323,571.2 5,565,224.4 3,645,066.1 3 Chicago 6,975 67,637,409.9 32,720,827.3 18,085,607.0 11,598,932.6 4 Rockford 272 3,718,824,6 2,002,673.4 1,220,463.5 809,708.7 5 Peoria 342 3,993,794.9 1.709,790.7 779,064.9 374,237.3 6 Davenport 363 4,098,968.0 1,695,598.8 747,482.0 348,180.0 7 Gary 633 10,398,080.5 7,161,109.3 5,736,761.7 5,082,255.9 8 Southhend 280 4,369,727.3 2,536,841.3 1,587,279.5 1,058,133.6 9 Fort Wayne 362 5,426,755.1 2,864,924.8 1,547,056.2 851,626.8 10 Indianapolis 1,111 14,211,255,3 6,753,371.9 3,342,917.3 1,721.411.1 11 Louisville 867 10,168,026.8 4,318,181.4 1,959,500.4 957,754.4 12 Lexington 267 3,105,752.3 1,329,239.7 626,243.3 330,927.5 13 Cincinnati 1,387 17,073,309.3 8,088,031.7 4,195,338.0 2,431,426.8 14 Dayton 853 11,310,543.2 5,800,887.0 3,322,440.5 2,137,559.9 15 Columbus 1,018 13,533,359.0 6,789,038.9 3,618,855.9 2,045,773.0 16 Lima 210 3,184,058.9 1,723,794.0 981,734.8 583,333.7 17 Toledo 763 12,290,285.1 7,602,623.2 5,150,496.5 3,791,381.7 18 Cleveland 2,064 27,086,132.1 14,313,725.1 8,492,774.4 5,790,823.1 19 Akron 679 9,922,076.2 5,924,298.4 4,225,829.7 3,548,527.6 20 Canton 394 5,928,170.5 3,622,127.6 2,706,932.6 2,454,235.2 21 Youngstown 537 7,465,661.7 4,164,916.7 2,690,474.1 1,965,986.4 22 Pittsburgh 2,401 26,319,022.7 11,563,901.3 5,583,747.4 2,923,027.0 23 Erie 264 3,334,222.3 1,556,964.8 788,282.5 431,226.0 24 Buffalo 1,349 16,059,157.2 7,073,314.4 3,378,061.4 1,745,882.3 25 Detroit 4,435 54,695,874.6 32,353,872.4 27,624,830.4 37,824,598.2 26 Fort Huron 36 544,583.5 323,309.9 218,443.8 165,450.6 27 Saginaw 220 3,125,985.2 1,677,042.8 1,009,663.6 686,306.2 28 Flint 509 7,770,950.2 4,660,230.4 3,170,013.1 2,402,370.6 29 Ann Arbor 234 4,100,881.0 2,914,876.1 2,443,133.3 2,313,455.2 30 Jackson 259 4,082,186.1 2,428,405.2 1,586,757.4 1,134,869.6 31 Lansing 424 6,378,655.4 3,609,999.4 2,220,751.0 1,478,274.1 32 Battle Creek 142 2,239,899.9 1,295,922.0 827,383.7 600,291.2 33 Grand Rapids 539 7,408,404.8 3,743,355.7 1,998,847.9 1,133,957.3 34 Kalamazoo 258 3,887,776.4 2,146,564.2 1,254,355.5 775,132.0 35 Windsor 259 5,566,960.3 7,237,629.7 13,640,856.2 29,181,146.5 36 Sarnia 79 1,201,016.3 711,649.1 481,164.7 362,938.4 37 London 286 3,697,853.7 1,773,267.4 910,843.5 497,237.6 38 Toronto 2,628 25,569,468.8 9,660,665.7 3,770,028.1 1,523,563.3 39 Hamilton 499 6,583,315.8 3,583,485.3 2,446,442.2 2,030,731.1 40 Niagara Falls 303 3,669,816.9 1,689,986.8 877,844.2 514,463.0 Correlation Coefficient 0,989 0.952 0.740 0.503 A9 APPENDIX VI.— Population Energy (Energy of Interchange) at each SMSA Based on the Time-Distance by the HSIPT System (b = 0.5). Unit: Square-Person/Hour Population SMSA (1,000s) b - 0.5 1 Madlaon 290 3,708,646.9 2 Milwaukee 1,404 20,657,337.3 3 Chicago 6,975 82,882,288.5 4 Rockford 272 3,842,294.2 5 Peoria 342 4,011,809.0 6 Davenport 363 4,090,235.8 7 Gary 633 11,878,748.0 8 Souchbend 280 4,524,736.8 9 Fort Wayne 362 5,529,078.0 10 Indianapolis 1,111 18,127,095.9 11 Louisville 867 10,835,327.6 12 Lexington 267 3,546,304.3 13 Cincinnati 1,387 22,534,584.6 14 Dayton 853 14,249,929.6 15 Columbus 1,018 17,687,S45.2 16 Lima 210 3,335,655.7 17 Toledo 763 14,798,779.5 18 Cleveland 2,064 34,318,007.0 19 Akron 679 11,640,044.9 20 Canton 394 6,355,912.2 21 Youngstown 537 8,333,870.1 22 Pittsburgh 2,401 31,212,224.0 23 Erie 264 3,486,381.7 24 Buffalo 1,349 16,092,508.1 25 Detroit 4,435 72,499,582.7 26 Port Huron 36 630,522.0 27 Saginaw 220 3,761,444.0 28 Flint 509 9,738,580.5 29 Ann Arbor 234 4,700,002.3 30 Jackson 259 4,706,099.8 31 Lansing 424 7,877,121.3 32 Battle Creek 142 2,524,004.6 33 Grand Rapids 539 10,123,928.3 34 Kalamazoo 258 4,679,902.0 35 Windsor 259 6,336,323.4 36 Sarnia 79 1,385,521.0 37 London 286 4,518,069.0 38 Toronto 2,628 30,313,974.7 39 Hamilton 499 6,757,707.6 40 Niagara Falls 303 3,733,657.5 A10 VII.— The 192 Nodal Accessibilities to Five Major Shopping Opportunities in the Lansing Metropolitan Area. m EJ Ai 2 1 - 1, , ...192; j - k " 0.5. j-1 1 4 7 2 8 2 .B 49 64896.8 97 47572.8 145 96802.8 2 49235.2 50 70384.1 98 49557.0 146 107445.2 3 49235.2 51 70384.1 99 45810.6 147 83945.6 4 51454.8 52 77667.7 100 47572.8. 148 90408,5 5 51454.B 53 77667.7 101 51814,6 149 99472.1 6 54009.8 54 B8162.9 102 54415.7 150 92464.5 7 54009.8 55 88162.9 103 49557,0 151 85363,6 B 56995.8 56 106242.0 104 51814.6 152 79566.5 9 55836.2 57 95261.3 105 57458.7 153 83063.9 10 57512.7 58 105258.8 106 61087.5 154 73901 .1 11 59131.8 59 116788.8 107 54415.7 155 73901 ,1 12 61142.3 60 138721.0 108 57458.7 156 67475.2 13 55935.7 61 95992.5 109 65523.9 157 67475,2 1 4 53659.8 62 85363.6 no 71134.0 158 62566.0 15 59250.7 63 118840.8 111 61087.5 159 62566.0 16 56562.8 64 99472.1 112 65523.9 160 58631.6 17 514 5 4 .B 65 56995.8 113 44231.7 161 75205.6 IB 54009.S 66 60552.9 114 45810.6 162 79668.4 19 54009.8 67 54415,7 115 42806.2 .163 68750.2 20 56995.8 68 57458.7 116 44231 .7 164 72071.5 21 56995.8 69 64896.8 1 17 47572.8 1 65 76086.9 22 60552.9 70 70384.1 118 49557.0 166 71538.6 23 60552.9 71 61087.5 119 45810,6 5 67 69352.2 24 64896.8 72 65523,9 120 47572.8 168 65704.6 25 63097.7 73 77667.7 121 51814.6 169 87475.2 26 65571.8 74 88162.9 122 54415.7 170 62566.0 27 68000.4 75 71134,0 123 49557.0 171 67566 * 0 28 71152.6 76 78586.7 124 51814.6 173 58631.6 29 63244.1 77 106242.0 125 57458.7 173 58631.6 30 60000.6 78 143417.7 126 61087.5 174 5537^.8 31 681 8 8 .B 79 89326.4 127 54415.7 175 55376.8 32 64163.1 80 107783.5 128 57458.7 176 52671 .3 33 56995.8 81 51814.6 129 164613.9 177 63724.8 34 60552.9 82 54415.7 1 30 21B4S2.7 17B 66321.7 35 60552,9 83 49557,0 131 118975.2 179 59866.5 36 64896.8 84 51814,6 132 142200.5 180 61769,4 37 64896.8 85 57458,7 133 169835.3 181 64163.1 38 70384.1 86 61087.5 134 125941.7 182 61164,0 39 70384,1 87 54415.7 135 125941.7 183 60000.6 40 77667.7 88 57458.7 136 116692.1 184 57481.7 41 74286.7 89 65523.9 137 96598.4 185 58631.6 42 78505.0 90 71134.0 138 85363.6 186 55376.8 43 82782,1 91 61087.5 139 92855.7 187 55376,8 44 88867.0 92 65523.9 140 81930.0 IBS 52621.3 45 74545.4 93 78586.7 141 76086.9 189 52621.3 46 69352.2 94 89326.4 142 69352.2 190 50246.9 47 83177.2 95 71134.0 143 73312.7 191 50246,9 48 76086.9 96 78586.7 144 67101.7 192 48171.8 (continued) All VII.— Continued. k = i 1 7599.7 49 14406.3 97 7686 ,3 145 32061.1 2 8243.9 50 17003.8 98 8343.3 146 397B8.9 3 8243.9 51 17003,8 99 7125i 8 147 24011 .7 4 9008.9 52 20833.6 100 7686.3 148 27961. 1 5 9008.9 53 20833.6 1*01 9124.3 149 34277.8 6 9933.0 54 27207,4 102 10068.6 150 31477.2 7 9933.0 55 27207.4 103 8343.3 151 25010.6 e 11072.7 56 41252.8 104 9124.3 152 77211 * 2 9 10596.5 57 31252.8 105 11234.3 153 24433.5 10 11248.9 58 38495.6 106 12711.6 154 18948.6 11 11890.6 59 47845.6 107 10068.6 155 18940,6 12 12723.0 60 69041.7 108 11234.3 156 15663.9 13 10634.3 61 31852.8 109 14648.8 157 15663.9 14 9784,4 62 25010.6 110 17311.7 158 13408.4 15 11939.1 63 50166.7 111 12711.6 159 13408.4 16 10876.8 64 34277.8 112 14648.8 160 11744.3 17 9008.9 65 11072.7 113 6641.9 161 19237.8 18 9933.0 66 12515.6 114 7125.8 162 21641.7 1? 9933.0 67 10068.6 115 6219.8 163 16061.7 20 11072.7 68 11234.3 116 6641,9 164 17680.6 21 11072.7 69 14406.3 117 7686.3 165 19786.2 22 12515.6 70 1700 3.B 118 8343.3 166 17693.9 23 12515.6 71 12711.6 119 7125.8 167 16400.6 24 14406.3 72 14648.6 120 7686.3 168 14826.6 25 13549.2 73 20833.6 121 9124.3 169 15663.9 26 14648.8 74 27207.4 122 10068.6 170 13408.4 27 15753.8 75 17311.7 123 8343.3 171 13408.4 28 17276.5 76 21237.8 124 9124,3 172 11744,3 29 13614.1 77 41252.8 125 11234.3 173 11744.3 30 12247.0 78 85345.6 126 12711.6 174 10458.9 31 15846.3 79 27761.7 127 10068.6 175 10458.9 3? 14018.3 80 42061.1 128 11234.3 176 9432.9 33 11072.7 81 9124.3 129 100041.7 177 13791.7 34 12515.6 02 10068.6 130 183600.0 178 14956.7 35 12515.6 83 0343.3 131 49138.9 179 12086.6 36 14406.3 84 9124.3 132 71466.7 180 12965.5 37 14406.3 85 11234.3 133 110383.3 181 14018.3 38 17003.8 86 12711.6 134 56633,3 182 12801.3 39 17003.8 87 10068,6 135 56633.3 183 12247.0 40 20833.6 88 11234.3 136 58527.8 184 11281.0 41 18833.6 89 14648.8 137 32254.4 185 11744.3 42 21087.4 90 17311.7 138 25010.6 186 10458.9 43 23457.4 91 12711.6 139 31065.6 187 10458.9 44 27152.8 92 14648.8 140 23463.5 188 9432.9 45 18977.9 93 21237.8 141 197B6.2 189 9432.9 46 16400.6 94 27761.7 142 16400.6 190 8593.6 47 23717.2 95 17311,7 143 18544.4 191 8593.6 48 19786.2 96 21237.8 144 15443.8 192 7893.5 {continued) A. 12 Appendix VII.— Continued. k = 1.5. 1 1224.5 49 3226.1 97 1243.9 145 10719.<4 2 1384.3 50 4158.0 98 1407.4 14(4 14954.6 3 1384.3 51 4158.8 99 1109.9 147 6907.6 4 1582.7 52 5695.0 100 1243.9 148 8719.1 w 5 1502.7 53 5695.0 101 1610.5 149 12055. 6 1834.4 54 8683.4 102 1866.4 150 11934. t 7 1834.4 55 0683.4 103 1407.4 151 7412. 8 2162.2 56 17394.2 104 1610.5 157 6464•h o-o > cnra n o i- a ^ ( c 9 2015.3 57 10400.6 105 2204.6 153 7588. 10 2206.3 58 14377.8 106 2657.8 154 4991. 1 1 2397.4 59 20273.5 107 1868,4 155 4991, 12 2656.6 60 35914.7 108 2204.6 156 3696, 13 2026.4 61 10773.2 109 3296.5 159 3696. 14 1787.7 62 7412.4 110 4253,8 150 2905. 15 2412.6 63 22212•4 111 2657.8 159 2905. 16 2096.8 64 12053.2 112 3296.5 160 2371. 17 1582.7 65 2162.2 113 998.5 161 4936. 18 1034.4 66 2603.9 162 5909. 114 1109.9 hr 19 1834.4 67 1860.4 115 904 . 7 163 3760. 20 2162.2 68 2204.6 116 998.5 164 4353. 21 2162.2 69 3226.1 117 1243.9 165 5184,1 22 2603.9 70 4158.8 118 1407.4 166 4476.0 23 2603.9 71 2657.8 119 1109.9 167 3899.1 24 3226.1 72 3296.5 120 1243.9 168 3393.9 25 2919.2 73 5695.0 121 1610.5 169 3696.5 26 3287.1 74 8683.4 122 1860.4 170 2905.8 27 3665.5 75 4253.8 123 1407.4 171 2905.8 20 4219.7 76 5828.5 124 1610.5 172 2371.6 29 2941.4 77 17394.2 125 2204.6 173 2371.6 30 2507.6 78 62211.6 126 2657,8 174 1987,6 31 3700.6 79 8881.9 127 1868.4 175 1907.6 32 3074.9 80 17713.2 128 2204.6 176 1699.2 33 2162.2 01 1610,5 129 66393.6 177 2909.6 34 2603.9 02 1868.4 130 166876.8 170 3382.0 35 2603,9 83 1407.4 131 20850.4 179 2451.3 36 3226.1 84 1610.5 132 37194.7 100 2727.0 37 3226.1 85 2204.6 133 80717.1 181 3074.9 38 4158.8 86 2657.8 134 26734.9 182 2706.1 39 4158.0 07 1868.4 135 26734,9 1 8 3 2 5 0 7 . 6 40 5695,0 00 2204.6 136 39173.2 184 2230.3 41 4802,9 89 3296.5 137 10966.9 185 2371,6 42 5711.3 90 4253.8 138 7412.4 186 1987,6 43 6704,1 91 2657.8 139 11217.0 187 1987.6 44 0400.1 92 3296.5 140 6965.2 188 1699,2 45 4865.1 93 5828.5 141 5184.1 189 1699.2 46 3899.1 94 8881.9 142 3899. 1 190 1475.6 47 6835.5 95 4253.8 143 4783.3 191 1475,6 40 5184.1 96 5628.5 144 3590.9 192 1299.7 (continued) A13 ii VII.— Continued. k = 2.0. l 197.8 49 728.8 97 201 .6 145 2 233.1 50 1029.9 98 23 7.9 146 3 233.1 51 1029.9 99 173. 1 147 4 279.0 52 1587.0 100 201 .6 148 5 279.0 53 1587.0 101 284.9 149 6 340.2 54 2868.7 102 347. 7 150 7 340.2 55 2868.7 103 237.9 151 8 424.4 56 7952.4 104 284.9 .152 9 384. 1 57 3507.9 105 434.3 153 10 4 33.9 58 5465.6 106 558.5 1 54 U 484.6 59 8892.0 107 347.7 155 12 5 56.5 60 19299.3 108 434. 3 156 13 387.0 61 3712.6 109 747.0 .157 14 327.3 62 2220.7 1 10 1055.9 158 15 488. 9 63 10307.9 111 558.5 159 16 405.2 64 4318.8 1 1.2 747,0 160 17 279.0 65 424.4 113 150.3 161 18 340.2 66 545. 3 114 173.1 162 19 3 4 0.2 67 3 4 7.7 115 131 .7 163 20 424.4 68 434.3 1 1.6 150.3 164 21 424.4 69 728.8 117 201 .6 165 22 545.3 70 1029.9 118 237,9 166 23 545.3 71 558.5 119 173.1 167 24 728.8 72 747.0 120 201 .6 168 25 630.9 73 1587,0 121 284.9 169 26 740.6 74 2868.7 122 347.7 170 27 856.3 75 1055.9 123 237.9 171 28 1036.3 76 1626.3 124 284.9 172 29 637.8 77 7952.4 125 434.3 173 30 515.0 78 52642.0 126 558.5 174 31 868.4 79 2932.0 127 347.7 175 32 677.1 80 8064.7 128 434,3 176 33 424.4 81 284.9 129 48243.7 177 34 545.3 82 347.7 130 158498.1 178 35 545.3 83 237.9 131 9121.9 179 3 6 728.8 84 284.9 132 19905.6 180 37 728.8 85 434.3 133 6530B.3 181 38 1029.9 86 558 .5 134 13181.9 182 39 1029.9 87 347.7 135 13181.9 183 40 1587.0 88 434.3 136 32610.5 184 41 1231.4 89 747.0 137 3792.5 IBS 42 1558.5 90 1055.9 138 2220.7 186 43 1931.2 91 558.5 139 4414 .6 187 44 2627.4 92 747.0 140 2153.7 188 45 1255.7 93 1626.3 141 1368.0 189 A6 931.7 94 2932.0 142 9 31.7 190 47 1990.8 95 1055.9 143 1260.8 191 48 1368.0 96 1626.3 144 850.1 192 (continued) A14 Appendix VII.— Continued. k = 2.5. J. 32.0 49 166. 1 97 32.7 145 1235.7 2 39.4 50 258.2 98 40.3 146 2194.4 3 39.4 51 258.2 99 27.0 147 579.4 4 49.3 52 450.9 100 32.7 148 866 • 9 rj 49.3 53 450.9 101 50.5 1 49 1574.5 6 63.3 54 980 .5 102 64.9 150 2419,1 7 63.3 55 980.5 103 40.3 151 672 . 1 8 83.7 56 3900,1 104 50.5 152 635. 1 9 73.3 57 1198.5 105 8 5 .9 153 8B6, 3 10 85.5 58 2108 .0 106 118.0 1 54 38.1 . 2 1 1 98.2 59 4041.0 107 64.9 155 381. .2 1 2 116.9 60 10610.8 108 85.9 156 217.9 13 74.1 61 1302.8 109 170,5 157 217.9 1A 6 0 .0 62 672.1 110 265.0 158 141 .7 15 99.3 63 4990.0 111 118.0 159 141.7 1 6 7 8 .5 64 1574.5 112 170.5 160 99. 4 17 49.3 65 8 3 .7 113 22.7 161 32S.2 18 63 .3 66 115.0 114 27.0 162 447. 1 19 63.3 67 64 .9 115 19.2 163 207. 5 20 0 3 .7 68 85.9 1 16 22.7 164 266 . 6 21 83 .7 69 166. 1 117 32 . 7 165 363 . 4 22 115.0 70 258.2 118 40.3 166 310. 1 23 115,0 71 118.0 119 27.0 167 223. 7 24 166.1 72 170.5 120 32. 7 168 186.0 25 136.7 73 4 5 0.9 121 50.5 169 217. 9 26 167.5 74 980.5 122 64.9 1 70 141.7 27 200.8 75 265.0 123 40.3 171 141.7 28 255,8 76 461.7 124 50.5 17? 99.4 29 138,8 77 3900.1 125 85.9 173 99.4 30 106, 1 78 48530.7 126 118.0 174 73. 3 31 204.7 79 999.5 127 64,9 175 73. 3 32 149.6 80 3937,2 128 85.9 176 36,0 33 83.7 81 50.5 129 38226.6 177 141.1 34 115.0 82 64.9 130 154142.5 178 1 74 . 2 35 115.0 83 40.3 131 4127.4 179 101 .2 36 166. 1 84 50 .5 132 10802.5 1.80 121 .4 37 166. 1 85 85.9 133 57052.9 101 149.6 38 258.2 86 118.0 134 6745.0 182 125. 1 39 258.2 87 64.9 135 6745.0 183 106. 1 40 450,9 88 B5.9 136 30344.5 184 89.3 41 317.3 89 170.5 137 1332.2 185 99.4 42 428. 1 90 265.0 138 672.1 186 73,3 43 560.4 91 118.0 139 1890.1 187 73 • 3 44 029.7 92 170.5 140 695.7 IBS 56.0 45 326.3 93 461 .7 141 363.4 189 56.0 46 223.7 94 999.5 142 223.7 190 44 . 1 47 505,7 95 265, 0 143 340.2 191 44 . 1 48 363.4 96 461.7 144 203.8 192 35. 4 (continued) A15 Appendix VII.— Continued. k = 3.0. 1 5 .2 49 38.2 97 5« 5 145 426.5 2 6 .7 50 65.6 98 6 .8 146 853.9 3 6 .7 51 65 .6 99 4 .2 147 169.2 4 8 .8 52 130,5 100 5 .3 148 276.0 5 8 .8 53 130,5 101 9.0 149 583. 1 6 11.8 54 346.0 102 12.2 150 1243.2 7 11.8 55 346.0 103 6 .8 151 205.3 8 16.6 56 2019,6 104 9 .0 152 215.0 9 14.0 57 414.7 105 17". 1 153 332.9 10 16.9 58 822.6 106 25. 1 154 111.0 11 19.9 59 1903.7 107 12.2 155 111.0 12 24,6 60 5926.3 108 17.1 156 54.6 13 14.2 61 465.2 109 39.2 157 54.6 14 11.0 62 205.3 110 6 7 .2 158 32.0 15 20.2 63 2519.5 111 25.1 159 32.0 - o o - o o O' o- o m cj -o rg o n ci -o 16 15.2 64 583. 1 112 39 .2 160 20.6 0 17 8.8 65 16,6 113 3.4 161 85.0 18 11.8 66 24.4 114 4 .2 162 12^> * 8 19 11.8 67 12.2 115 2 .8 163 48.9 20 16.6 68 17.1 116 3 .4 164 66 .3 21 16.6 69 38.2 117 5 .3 165 97.2 24,4 70 65.6 118 6 .8 166 85.3 23 24.4 71 25.1 119 4 .2 167 54 .O 24 38.2 72 39.2 120 5 .3 168 45.0 25 29.7 73 130.5 121 9 .0 169 54.6 26 38.0 74 346.0 122 12.2 170 32,0 27 47.2 75 67.2 123 6 .8 171 32.0 28 63.4 76 133 . 4 124 9.0 172 20.6 29 3 0 .3 77 2019.6 125 17.1 173 20.6 30 21 .9 78 46695.4 126 25. 1 174 14.2 31 4 8 .5 79 351 . 5 127 12.2 175 14.2 32 3 3 .2 80 2031.5 128 17.1 176 10.3 33 16.6 81 9 .0 129 32614.5 177 30.7 34 24.4 82 12.2 130 151811.2 178 39.7 35 24.4 83 6.8 131 1935.0 179 20. 6 36 3 8 .2 84 9.0 132 6044.1 180 25.7 37 38.2 85 17.1 133 52527.9 181 33.2 38 6 5 .6 86 25.1 134 3557.4 182 27.4 39 65.6 87 12.2 135 3557.4 183 21 .9 40 130.5 88 17. 1 136 29548.4 184 18.1 41 82. 1 89 39.2 137 474.7 185 20. 6 42 118.3 90 67.2 138 205.3 186 14.2 43 163.8 91 25. 1 139 869,3 187 14.2 44 264.2 92 39.2 140 234.8 188 10.3 45 85.3 93 133.4 141 97.2 189 10.3 46 54.0 94 351 .5 142 54.0 190 7. 7 w j &(!o u o j- s. o> o io :j > jj 'j a m o 'J j. o- o v) <) ii o 'j iJ oi 47 174.0 95 67.2 143 94.1 191 7 * / 48 97.2 96 133.4 144 49,6 192 5 .9 A16 VIII.— The 192 Nodal Accessibilities to Six Major Employment Opportunities in the Lansing Metropolitan Area. Ai - 2 ------where i - 1 . .. k - 0.5 J-i DU 1 19864.4 49 27519.9 97 20259.0 145 48009.5 2 20703.6 50 29946.5 98 21151.7 146 42951,2 3 20703.6 51 29946.5 99 19471.2 147 38903,5 4 21660.3 52 33199.3 100 20259.0 148 36555.8 5 21660.3 53 33199.3 101 22175.5 149 40143.0 6 22765.2 54 37946.2 102 23366.1 150 36131.1 7 22765.2 55 37946.2 103 2115.1 . 7 151 34667.0 8 24061.7 56 46227.5 104 22175.5 152 31742.8 9 23 5 9 7 .B 57 41835.2 105 24775.5 153 32977.7 10 23225.2 58 39630.6 106 26481.6 154 29685,8 11 25045.1 59 53315.3 107 23366.1 155 29685.8 12 24597.0 60 48683.1 108 24775.5 156 27282.9 13 22673.8 61 37622,3 109 28608.8 157 27282.9 14 21691.9 62 33395,7 110 31372.5 158 25406.8 15 23953.9 63 45937.8 111 26481.6 159 25406.8 16 22803.1 64 38339.8 112 28608.8 160 238B2.3 17 21660.3 65 24061.7 113 18769.3 161 3383^.6 18 22765.2 66 25613.5 114 19471.2 162 32413.0 19 22765.2 67 23366.1 115 18138.7 163 30419.0 20 24061.7 68 24775.5 116 18769.3 164 29436.2 21 24061.7 69 27519.9 117 20259.0 165 31025.4 22 25613,5 70 29946.5 118 21151.7 166 28810.1 23 25613.5 71 26481.6 119 19471.2 167 28359.3 24 27519,9 72 28608.8 120 20259.0 168 26609.9 25 26800.3 73 33199.3 121 22175.5 169 27282.9 2 6 26247.4 74 37946.2 122 23366.1 170 25406.8 27 28993.0 75 31372.5 123 2115.1 .7 171 25406.8 28 28287.2 76 35192.0 124 22175.5 172. 2388.7. 3 29 25484.2 77 46227.5 125 24775.5 173 23882.3 30 24108.5 78 63966.8 126 26481.6 174 22608.7 31 27361.6 79 41055.9 127 23366.1 175 22608.7 32 25673.6 80 52401.8 128 24775.5 176 21522.6 33 24061.7 81 22175.5 129 79939,0 177 27889.1 34 25613.5 82 23366.1 130 68498.8 178 27159.9 35 25613.5 83 21151.7 131 67968.1 179 25913.2 36 27519.9 84 22175.5 132 55256.9 180 25344,7 37 27519.9 85 24775.5 133 63602.6 181 26292.1 38 29946,5 86 26481.6 134 47401.9 182 24065.6 39 29946.5 87 23366.1 135 50271.7 183 24625.7 40 33199.3 88 24775.5 136 43693.5 184 23433.5 41 31846.9 89 28608.8 137 37342.5 185 23R82.3 42 30901.8 90 31372.5 138 33395.7 186 22608.7 43 35793.9 91 26481.6 139 36276.1 187 22608.7 44 34434.7 92 28608.8 140 32526,4 188 21522.6 45 29748.0 93 35192.0 141 30065.8 189 21522.6 4 6 27601.4 94 41055.9 142 27601.4 190 20581,5 4 7 32944.9 95 31372.5 143 29400.5 191 20581.5 48 30065.8 96 35192.0 144 27076.6 192 19755.3 (continued) A17 ii. VIII.— Continued. k = 1.0. 1 3228.0 49 6229.4 97 3357.7 145 20083. 3 2 3507.8 50 7398.4 98 3661.7 146 15277.8 3 3507.8 51 7398,4 99 3100.7 147 12694.4 4 3841.3 52 9141.7 100 3357.7 148 10991.7 5 3841.3 53 9141.7 101 4026.8 149 13491, / 6 4245.9 54 12080.2 102 4473.9 150 11283. 3 7 4245.9 55 12080.2 103 3661.7 151 9950.0 B 4747.4 56 18583.3 104 4026.8 152 8429.4 9 4556.9 57 14583.3 105 5034.7 153 9155.e 10 4410.5 58 13011.1 106 5759.9 154 7316, 0 11 5136.2 59 24400.0 107 4473.9 155 7316. 0 12 4949,0 60 20083.3 108 5034.7 156 6143. 0 13 4211.6 61 11925.0 109 6736.5 157 6143, 0 14 3853«5 62 9283.3 110 8128.6 158 5310. 3 15 4704.9 63 18611.1 111 5759.9 159 5310. 3 16 4261.6 64 12425.0 112 6736.5 160 4683. 1 17 3841.3 65 4747.4 113 2B80.5 161 9491 .7 IS 4245.9 66 5386.2 114 3100.7 162 8616. y 19 4245*9 67 4473.9 115 2689.6 163 7628.6 20 4747.4 68 5034.7 1 16 2880.5 164 7096,O 21 4747.4 69 6229.4 117 3357.7 165 7929.4 22 5386.2 70 7398.4 118 3661.7 166 6873,B 23 53B6.2 71 5759.9 119 3100.7 167 6607. 1 24 6229.4 72 6736.5 120 3357.7 168 5836.3 25 5886.5 73 9141.7 121 4026 ,8 169 6143. 0 26 5638.9 74 12080.2 1 T? 4473.9 170 5310. 3 27 6898.4 75 8128.6 123 3661.7 171 5310. 3 28 6555.6 76 10291.7 124 4026.8 172 4683. 1 29 5332.3 77 18583.3 125 5034.7 173 4683. 1 30 4768.4 78 39400.0 ' 126 5759.9 174 4191 6. 31 6159.5 79 14194.4 1 127 4473.9 175 4191.6 32 5415.7 80 24083.3 128 5034.7 176 3795.2 33 4747.4 81 4026.8 129 59000.0 177 6393, 7 34 5386.2 82 4473.9 130 42500.0 178 6035.7 35 5386.2 83 3661 .7 131 44666. 7 179 5509.9 36 6229.4 84 4026.8 132 25916.7 180 5253. 0 37 6229.4 85 5034.7 133 40583.3 181 5669.6 38 7398.4 86 5759.9 134 19944.4 182 5082.7 39 7398.4 87 4473.9 135 21944.4 183 4968.4 40 9141 .7 88 5034.7 136 18491.7 184 4506.6 41 8341.7 89 6736.5 137 11744.0 185 4683. 1 42 7835.7 90 8128.6 138 9283.3 186 4191 .6 43 10580.2 91 5759.9 139 11239.0 187 4191. 44 9758.3 92 6736.5 140 8841.5 188 3795. 7305.6 93 10291.7 141 7472.2 189 3795, 6273.8 94 14194.4 142 6273.8 190 3468.3 9016.7 95 8128.6 143 7154.2 191 3468.3 7472.2 96 10291.7 144 6040.4 192 3193. 9 (continued) Aia Appendix VIII.— Continued. k = 1.5. 1 525.6 49 1420,6 97 557.7 145 8978.2 2 595.8 50 1846.9 90 435.5 146 5512.6 3 595.8 51 1846.9 99 494.7 147 4258. 1. 4 683.2 52 2557,3 100 557.7 148 3330.0 5 683,2 53 2557.3 101 733.4 149 4649.5 6 794.7 54 3953,2 102 859.8 150 3828.6 7 794.7 55 3953.2 103 635.5 151 2896.1 8 940.8 56 7979,9 104 733.4 152 2300,8 9 882.1 57 5182.5 105 1027.9 153 2646.7 10 838.9 58 4338.0 106 1260.3 154 1839.8 11 1056.4 59 11702.5 107 859.8 155 1839.0 12 997.8 60 8635.1 108 1027.9 156 1400.5 13 7B5.2 61 3909.8 109 1598.8 157 1400.5 14 686.8 62 2633.0 110 2129.6 158 1119.5 15 928.4 63 8186.8 111 1260.3 159 1119.5 16 799.7 64 4173.8 112 1598.8 160 924. 1 17 683.2 65 940.8 113 442.8 161 2703.8 18 794.7 66 1139.0 114 494.7 162 2301.4 19 794.7 67 859,8 115 399.4 163 1932.3 20 940.8 68 1027,9 116 442.8 164 1716.1 21 940.8 69 1420.6 117 557.7 165 2044.9 22 1139.0 70 1846.9 118 635.5 166 1668.0 23 1139.0 71 1260.3 119 494.7 167 1 5 4 9 .i 24 1420,6 72 1598.8 120 557,7 168 1294.1 25 1297,8 73 2557.3 121 733.4 169 1400.5 26 1214.7 74 3953.2 122 859.8 170 1119,5 27 1649.6 75 2129,6 123 435.5 171 1119.5 28 1524,8 76 3060.6 124 733.4 172 924. 1 29 1122.5 77 7979.9 125 1027,9 173 924. 1 30 948.0 78 28477.8 126 1260.3 174 780.9 31 1398,0 79 5049,8 127 859.8 175 780.9 32 1150.0 80 11775.6 128 1027.9 176 671,8 33 940.8 81 733.4 129 48247.0 177 1476.0 34 1139,0 82 859.8 130 29530,7 178 1344.4 35 1139.0 83 635,5 131 34188.5 179 1177.7 36 1420.6 84 733.4 132 12628.9 180 1090.7 37 1420.6 85 1027.9 133 31266.0 181 1228.3 38 1846.9 86 1260.3 134 9119.3 182 1047.0 39 1846.9 87 859,8 135 10174.9 183 1006.0 40 2557.3 88 1027.9 136 10241.3 184 871 .7 41 2200,5 89 1598.8 137 3814.1 185 924. 1 42 1997.4 90 2129.6 138 2633.0 186 780.9 43 3161.5 91 1260.3 139 3701.3 187 780.9 44 2788.4 92 1598.8 140 2473.3 188 671 .0 45 1815.4 93 3060.6 141 1881.2 189 671 .8 46 1438.9 94 5049.8 142 1438.9 190 586.3 47 2514.1 95 2129,6 143 1768.7 191 586.3 48 1881.2 96 3060.6 144 1361.4 192 517.7 (continued) A19 VXIX.— Continued. k = 2.0. 1 85.8 49 326.3 97 92.8 145 2 101.4 50 465.8 98 110.6 146 3 101.4 51 465.8 99 79.1 147 4 121.9 52 726.6 100 92.8 148 5 121 .9 53 726.6 101 134.0 149 6 149.3 54 1329.4 102 165. 8 150 7 149.3 55 1329.4 103 110.6 151 8 187.2 56 3652.8 104 134.0 152 9 171 .1 57 1875.0 105 210,8 153 10 159.8 58 1469.6 106 277.3 154 11 217.9 59 5850.7 107 165.8 155 12 201.6 60 3879.6 108 210.8 156 13 146.9 61 1326.7 109 382.3 157 14 122.8 62 761 .9 110 563.8 158 15 184.1 63 3887.3 111 277.3 159 16 150.7 64 1451.7 112 382.3 160 17 121.9 65 187.2 113 68,2 161 18 149.3 66 242.2 114 79.1 162 19 149.3 67 165.8 115 59.4 163 20 187.2 68 210. B 116 6 8 .2 164 21 187.2 69 326.3 117 92. 8 165 *72 242.2 70 465.8 118 110.6 166 23 242.2 71 277.3 119 79.1 167 24 326.3 72 382.3 120 92.8 168 25 287.2 73 726.6 121 134.0 169 26 262.4 74 1329.4 122 165.8 170 27 396.4 75 563.8 123 110.6 171 28 356.0 76 924.3 124 134.0 172 29 237.7 77 3652.8 125 210.8 173 30 189.4 78 23350.7 126 277.3 174 31 320.0 79 1842.4 127 165.8 175 32 245.9 80 6046.3 128 210.8 176 33 187,2 81 134.0 129 42541.7 177 34 242.2 82 165.8 130 22051.9 178 35 242,2 83 110.6 131 29231.5 179 36 326.3 84 134,0 132 6375.0 180 37 326.3 85 210.8 133 27338.0 181 38 465.8 86 277.3 134 4479.9 182 39 465.8 87 165.8 135 4979.9 183 40 726.6 88 210.8 136 7474.7 184 41 584.4 89 382.3 137 1278.3 185 42 511.8 90 563.8 138 761 .9 186 43 954.4 91 277.3 139 1316.6 187 803.6 92 382.3 140 716.0 188 456.7 93 924.3 141 479.8 189 333.0 94 1842.4 142 333.0 190 714,5 95 563.8 143 445. 3 191 479.8 96 924.3 144 310.4 192 (continued) A2 0 VIII.— Continued. k = 2.5. 1 14*0 49 75 .5 97 15.5 145 2 17.3 50 118.7 98 19.3 146 3 17.3 51 118.7 99 12.7 147 4 21.8 52 209.6 100 15.5 148 5 21.8 53 209.6 101 24 .5 149 6 28.1 54 458.9 102 32.1 150 7 28.1 55 458,9 103 19.3 151 8 37.4 56 1770.2 104 24.5 152 9 33,3 57 689.6 105 43.4 153 10 30.5 58 506.0 106 61.4 154 11 45.1 59 3029.4 107 32.1 155 12 40.8 60 1821.9 108 43.4 156 13 27.6 61 465.3 109 92.1 157 14 22.0 62 224.8 110 150.7 158 15 36,7 63 1964.7 111 61.4 159 16 28,5 64 521 .3 112 92. 1 160 17 21,8 65 37.4 113 10,5 161 IS 28..1 66 51 .8 114 12.7 162 19 28,1 67 32.1 115 8 .8 163 20 37,4 68 43.4 116 10.5 164 21 37,4 69 75.5 117 15.5 165 22 51.8 70 118.7 118 19.3 166 23 51,8 71 61 .4 119 12.7 167 24 75,5 72 92.1 120 15.5 168 25 63.8 73 209.6 121 24,5 169 26 56.8 74 458.9 122 32.1 170 27 95.7 75 150.7 123 19.3 171 28 83.4 76 283.0 124 24.5 172 29 5 0 .7 77 1770.2 125 43.4 173 30 38.1 78 20825.2 126 61.4 174 31 73.9 79 686.9 127 32. J 175 32 52.9 80 3218.7 128 43.4 176 33 37.4 81 24.5 129 39440-7 177 34 51 .8 82 32.1 130 19317.9 178 35 51 ,8 83 19.3 131 26775.0 179 36 75.5 84 24.5 132 3318.7 180 37 75.5 85 43.4 133 25605.7 181 38 118.7 86 61.4 134 2324,9 182 39 118.7 87 32. 1 135 2548.9 183 40 209,6 88 43.4 136 6524.5 184 41 156.2 89 92. 1 137 441.1 185 42 131.9 90 150,7 138 224.8 186 43 290.9 91 61 .4 139 511.0 187 44 233.6 92 92. 1 140 215.7 188 116.3 93 283.0 141 124.0 189 77.8 94 686.9 142 77.8 190 207.0 95 150.7 143 114.4 191 124.0 96 283.0 144 71 .6 192 (continued) A21 Appendix VIII.— Continued. k = 3.0. 1 2.3 49 17.6 97 2.6 145 1115.9 2 3.0 50 30,5 98 3.4 146 280.8 3 3,0 51 30.5 99 2.0 147 187. 8 4 3.9 52 61.3 100 2.6 148 96. 7 5 3.9 53 61 .3 101 4 .5 149 218.3 6 5.3 54 162.4 102 6 .2 150 291 .3 7 5 .3 55 162.4 1(^3 3 .4 151 77. 3 8 7 .5 56 899.4 .1yJ4 4 .5 152 61 .3 9 6 .5 57 257.5 105 9 .0 153 89 .7 10 5 ,8 58 177. 1 106 13.6 154 34.3 11 9 .3 59 1613.4 107 6.2 155 34.3 12 8 .3 60 892,1 108 9 .0 156 18.2 13 5 .2 61 168.1 109 22.3 157 18.2 14 4 .0 62 67.6 110 40.6 .158 11.1 15 7 .3 63 1039.6 111 13*6 159 11.1 16 5.4 64 192.5 112 22.3 160 7.4 17 3.9 65 7.5 113 1.6 161 68.4 18 5.3 66 11.1 114 2.0 162 45. 1 19 5.3 67 6 .2 115 1.3 163 33.3 20 7.5 68 9 .0 116 1.6 164 24.7 21 7.5 69 17.6 117 2 .6 165 36,9 22 11.1 70 30.5 118 3 .4 166 27.2 23 11.1 71 13.6 119 2 ,0 167 20.7 24 17.6 72 22.3 120 2 .6 168 15.3 25 14.2 73 61 .3 121 4 .5 169 18.2 2 6 12.3 74 162.4 122 6 .2 170 11.1 2 7 23.2 75 40.6 123 3 .4 171 11.1 28 19.6 76 87.7 124 4 .5 172 7.4 29 10.9 77 899.4 125 9 .0 173 7.4 30 7 .7 78 19530.1 126 13.6 174 5 .2 31 17.2 79 260.7 127 6 .2 175 5 .2 32 11.5 80 1757.8 128 9.0 176 3.8 33 7 .5 81 4 .5 129 37725.7 177 18.9 34 11.1 82 6.2 130 17406.4 178 15.1 35 11.1 83 3 .4 131 25508.7 179 11.9 36 17.6 84 4 .5 132 1772.4 180 9.9 37 17.6 85 9.0 133 24805.3 181 12.8 38 3 0 .5 86 13.6 134 1253.6 182 9.7 39 30.5 87 6.2 135 1350.8 183 a. 5 40 61.3 88 9.0 136 6190.5 184 6.6 41 42.0 89 22.3 137 156.2 185 7.4 42 34.2 90 40.6 138 67.6 186 5.2 43 89.4 91 13.6 139 216.2 187 5.2 44 68 .5 92 22.3 140 67.9 188 3 .8 45 30.0 93 87.7 141 32.4 189 3 .8 46 18.3 94 260.7 142 18.3 190 2 .9 47 61 .1 95 40 .6 143 30.1 191 2 .9 48 32.4 96 87.7 144 16.8 192 2 .2 A22 IX.— The 192 Nodal Accessibilities to Nine Major Urban Centers (Urban Functions) in the Lansing Metropolitan Area. m Ej A* - I ---- £- whore i - 1.....192; j - 1,...,9; k - 0.5 1 3822.6 49 5212,4 97 3936.6 145 7957.9 2 3982.8 50 5659.3 98 4155.4 146 7409.7 3 3993.0 51 5642.4 99 3770.6 147 6864,7 4 4179.4 52 6266.3 100 3948.2 148 6576.5 5 4152.4 53 6137.4 101 4474.6 149 7480.9 6 4358.4 54 6877.8 102 5031.9 150 6476.6 7 4381.5 55 6871.4 103 4169.6 151 6633.1 8 4639.5 56 7972.9 104 4492.6 152 5896.2 9 4614.3 57 8120.9 105 4929.9 153 6018.0 10 4488.7 58 7425.0 106 5089.2 154 5537.0 U 5004.5 59 10327.3 107 4567.6 155 5532.4 12 4792.8 60 8851.2 108 4762.4 156 5133.9 13 4423.3 61 7410.7 109 5350.2 157 5082.2 14 4197.2 62 6416.6 110 5742.8 158 4737.7 15 4701.4 63 9248.7 H I 5007.8 159 4779.1 16 4428.2 64 7367.0 112 5370.6 160 4493.7 17 4190.9 65 4537.2 113 3631.8 161 6222.9 18 4413.6 66 4829.3 114 3784.9 162 6088.1 19 4371.1 67 4335.2 115 3498.6 163 5677.2 20 4617.1 68 45B7.1 116 3631.8 164 5533.2 21 4666.4 69 5190.9 117 3966.2 165 6276.0 22 5033.2 70 5659.9 118 4193.5 166 5550.9 23 4884.7 71 4892.1 119 3784.9 167 5562.9 24 5235.9 72 5274.8 120 3966.2 168 5090.8 25 5640.5 73 6266.5 121 4325.2 169 5197.0 26 5217.2 74 7264.0 122 4522.8 170 4847.2 27 5735.5 75 5685.3 123 4094.9 171 4853.8 28 5462.2 76 6296.6 124 4263.2 172 4567.0 29 5052.4 77 8101.8 125 4782.8 173 4545.4 30 4707.2 78 10335.2 126 5209.2 174 4297.7 31 5363.7 79 6890.9 127 4457.3 175 4314.8 32 4970.7 80 7978.0 128 4732.0 176 4102.6 33 4593.1 81 4168.8 129 15823.9 177 5319.8 34 4B78.9 82 4394.5 130 12151.0 178 5152.0 35 4867.1 83 4036.0 131 10286.8 179 4901.3 36 5214.5 84 4248.7 132 8862.3 180 4791.9 37 51B6.6 85 4666.9 133 13431.4 181 5115.3 38 5594.1 86 5011.5 134 9231.6 182 4762.8 39 5588.3 87 4513.9 135 9293.6 183 4755.1 40 6109.5 88 4892.8 136 7417.8 184 4476.1 41 6150.5 89 5299.3 137 7176.3 185 4569,2 42 5882.0 90 5715.1 138 6443.5 186 4329.0 43 6873.9 91 5041.3 139 6623.3 187 4320.2 44 6492.9 92 5336.4 140 6142.7 188 4116.6 45 5804.6 93 6164.8 141 5750.2 189 4114.2 46 5316.3 94 6856.1 142 5258.3 190 3939.8 47 6433.1 95 5680.3 143 5495.6 191 3931.8 5775.5 96 6178.6 144 5047.8 192 3770.7 (continued) A23 XX.— Continued. k = 1. 1 632.4 49 1182.9 97* 689.9 145 2827.8 2 688.7 50 1408.9 98 788.7 146 2389.8 3 692.0 51 1398.5 99 625.9 147 2063.3 4 761.8 52 1776.2 100 694.7 148 1879.0 5 751.6 53 1650.4 101 989.7 149 2516.6 6 835.1 54 2104.5 102 1617.5 150 1851.5 7 843.3 55 2084.5 103 795.0 151 1985.2 8 962.2 56 2849.2 104 998.3 152 1530.5 9 953.8 57 3092.9 105 1165.2 153 1593.8 10 883.4 58 2431.6 106 1154.8 154 1369.7 11 1184.2 59 5048.1 107 942.5 155 1338.6 12 1022.1 60 3467.8 108 1001.9 156 1155.1 13 852.8 61 2476.9 109 1252.3 157 1131.5 14 764.8 62 1820.3 110 1444.0 158 976.5 15 969.8 63 4089.4 111 1104.4 159 993. B 16 854.6 64 2449.7 112 1302.7 160 875.2 17 765.6 65 890.9 113 577.7 161 1695.0 18 856.2 66 1012.2 114 632.1 162 1658.6 19 827,9 67 812.6 115 533.5 163 1411.5 20 927.7 68 912.1 116 577.7 164 1339.9 21 972.3 69 1175.1 117 703.3 165 2041.2 22 1194.3 70 1410.4 118 807.5 166 1405.8 23 1042.7 71 1041.6 119 632.1 167 1412.7 24 1213.3 72 1219.6 120 703.3 168 1143.2 25 1817.7 73 1776.9 121 835.0 169 1190.8 26 1270.6 74 2650.7 122 911.4 170 1028.9 27 1516.6 75 1419.9 123 738.7 171 1031.2 28 1314.0 76 1788.9 124 798.4 172 908.9 29 1135.0 77 2975.1 125 1052.3 173 898.8 30 971.9 78 5034.3 126 1447.7 174 800.5 31 1260.2 79 2089.4 127 878.8 175 807.8 32 1076.0 80 2941.8 128 1029.2 176 728.2 33 912.7 81 753.5 129 13217.2 177 1279.7 34 1033.6 82 840.8 130 6705.8 178 1165.5 35 1026.4 83 712.1 131 4993.1 179 1054.7 36 1184.4 84 796.6 132 3465.4 180 998.7 37 1168.7 85 955.1 133 9924.0 181 1159.1 38 1367.6 86 1118.2 134 4083.4 182 992.4 39 1358.5 87 917.4 135 4114.4 183 9B9.3 40 1634.6 88 1148.2 136 2480.9 184 871.3 41 1676.2 89 1229.4 137 2322.3 185 909.3 42 1506.2 90 1432.6 138 1866.9 186 814.0 43 2109.8 91 1130.8 139 1979.5 187 809.8 44 1837.8 92 1246.3 140 1818.6 188 734.1 45 1475.5 93 1652.0 141 1459.1 189 733.0 46 1230.8 94 2055.7 142 1210.2 190 667.4 47 1828.0 95 1398.9 143 1352.5 191 668.2 48 1459.6 96 1658.1 144 1118.2 192 613.7 (continued) A2 4 Appendix IX.— Continued. k = 1.5. 1 105.9 49 273.3 97 128.7 145 1039.9 2 121.1 50 362.3 98 167.8 146 779.0 3 121.9 51 357.4 99 108.2 147 631.9 4 142.1 52 546.3 100 130.2 148 544.0 5 139.2 53 453.4 101 287.4 149 882.8 6 166.0 54 670.8 102 975.5 150 543.2 7 168.3 55 649.0 103 169.9 151 634.9 3 213.7 56 1054.6 104 290.5 152 409.0 9 211.6 57 1341.5 105 338.7 153 432.9 10 179.4 58 817.8 106 277.2 154 362.6 11 336.3 59 2638.3 107 211.6 155 330.6 12 231.2 60 1383.6 108 219.4 156 267.3 13 167.6 61 864.6 109 300.1 157 2S8.9 14 141.3 62 530.3 n o 372.4 153 204. 7 15 205.8 63 1976.0 111 253.2 159 210.2 16 168.1 64 B51.2 112 349.2 160 172.5 17 143.1 65 176.6 113 94.8 161 472.1 18 172.2 66 215.0 114 110.3 162 487.8 19 158.8 67 153.7 115 83.3 163 360.2 20 189.7 68 183.6 116 94.8 164 334.0 21 216.6 69 271.1 117 133.3 165 972.5 22 339.0 70 362.9 118 174.8 166 398.1 23 227.9 71 225.7 119 110.3 167 401.0 24 293.7 72 289.6 120 133.3 163 267.5 25 979.9 73 546.7 121 171.2 169 283.7 26 362.7 74 1289.0 122 195.2 170 224.5 27 451.2 75 365.6 123 138.5 171 225.2 28 327.2 76 550.2 124 154.8 172 184.6 29 268.2 77 1147.5 125 268.4 173 180.9 30 206.4 78 2618.5 126 661.3 174 151.0 31 302.4 79 648.9 127 183.0 175 153.4 32 236.4 80 1047.0 128 260.5 176 130.7 33 183.1 81 138.2 129 12257.6 177 341.3 34 222.1 82 164.1 130 3796.4 178 273.3 35 219,0 83 129.3 131 2593.9 179 235.5 36 274.0 84 156.2 132 1378.7 180 212.3 37 266 * 8 85 201. 7 133 8608.7 181 275.1 264.1 38 340.8 86 134 1974.8 182 212.3 39 334.9 87 202.6 135 1986.9 183 211-3 40 446.3 88 332.8 136 866.4 184 ■ 172.6 41 472,3 89 292.5 137 784.1 185 184.5 42 390.8 90 369.6 138 570.2 186 155.5 43 675.6 91 268.2 139 629.3 187 154.0 44 527.1 92 298.3 140 702.2 188 132.2 45 382.0 93 448.5 141 380.6 189 132.1 46 288.3 94 627.5 142 283.9 190 114.4 47 533.0 95 348.2 143 357.1 191 114.7 48 375.4 96 450.4 144 255.0 192 100.6 (conClnued) A25 Appendix IX.— Continued. k = 2.0. I 18.0 A9 6A.5 97 26.A 1A5 393.0 2 21.7 50 97.0 98 42.2 1A6 255.8 3 21.9 51 9A.9 99 19.8 IA7 196.7 4 27.A 52 188.7 100 26.8 1 AS 159.7 5 26.6 53 127.3 101 115.0 1A9 320.8 6 3A.8 5A 22A.6 102 853.9 150 163.0 7 35.3 55 206.6 103 A2.8 151 221.2 8 52.6 56 A00.9 10A 116.0 152 112.9 9 52.1 57 705.7 105 128.5 153 120.5 10 38.1 58 283.5 106 71.8 15A 107. A U 122.7 59 1A37.A 107 53.5 155 83.3 12 57.1 60 557.6 108 50.5 1S6 6A.2 13 33.7 61 313.0 109 73.9 157 61.5 1A 26.5 62 158.1 110 99.0 158 A3.8 15 A5.3 63 1019.1 ill 61.3 159 AS.A 16 33.8 6A 306.8 112 110.A 160 3A.5 17 27.6 65 35.A 113 16.2 161 134.7 18 36. A 66 A6.3 UA 20.A 162 162.1 19 30.9 67 29.A 115 13.A 163 94.9 20 39.6 68 37.5 116 16.2 16A 86.7 21 53.3 69 63.9 117 27.8 165 698.7 22 123.3 70 97.1 118 AA. 5 166 133.6 23 51.3 71 A9.3 119 20.A 167 134.7 2A 75.A 72 70.9 120 27.8 168 66.A 25 79 A. 0 73 188.9 121 38.0 169 71.3 26 129.9 7A 877.3 122 A5.7 170 50.7 27 158.A 75 97.7 123 27.3 171 51.0 28 85.3 76 189.8 12A 31.5 172 38.5 29 67.9 77 A62.A 125 87.1 173 37.2 30 A5.A 78 1A20.0 126 A95.7 17A 28.9 31 7A.1 79 205.9 127 A1.6 175 29.6 32 52.7 30 396.2 128 8A.6 176 23.7 33 37.1 31 25.8 129 11890.2 177 108.0 3A AS.A 32 32.9 130 2173.0 178 67.5 35 A7.3 83 2A.A 131 1A07.5 179 55.7 36 6A.7 8A 32.7 132 55A.1 180 46.3 37 61.7 85 AA. A 133 8098.2 181 69.5 38 86.5 86 67.6 13A 1019.1 182 46.9 39 83.6 87 50.6 135 1023.A 183 46.7 AO 12A.3 88 126.7 136 313.7 18A 34.9 38.3 A1 137.8 89 71.7 137 27A.2 185 A2 102.6 90 98.9 138 185.6 186 30.2 227.2 29.8 A3 91 68.7 139 216.a 187 AA 152.9 92 73.5 1A0 A07.5 188 24.2 24.1 A5 100.A 93 123.2 1A1 102.0 189 19.8 A6 6B.6 9A 19A.6 1A2 67.8 190 19.9 A7 158.9 95 87.5 1A3 105.8 191 A8 98.1 96 123.7 1AA 60.A 192 16.6 (continued) A2 6 Appendix IX.— Continued. k = 2.5. 1 3.1 49 15.6 97 6.1 145 151.7 2 4.0 50 27.3 96 12.7 146 84.4 3 4.0 51 26.5 99 3.9 147 62.0 4 5.5 52 75.0 100 6.2 148 47.6 5 5.3 53 36,5 101 57.2 149 119.9 6 7.8 54 80.0 102 830.6 150 49.9 7 7.9 55 67.1 103 12.9 151 85.4 8 14.7 56 155.4 104 57.5 152 32.3 9 14.6 57 467.5 105 60.5 153 34.4 8.6 58 102.2 106 20.4 154 37.4 11 56,0 59 802.6 107 15.5 155 21.5 12 15.8 60 226.0 108 12.4 156 16.2 13 7.0 61 116.7 109 18.7 157 15.3 14 5.1 62 48.1 110 27.3 158 9.6 15 10.4 63 549.2 111 16.0 159 10.0 16 7.0 64 113.9 112 42.9 160 7.0 17 5.5 65 7.2 113 2.9 161 39.5 18 8.2 66 10.1 114 4.1 162 63.1 19 6.1 67 5.7 115 2.2 163 26.0 20 8.5 68 7.8 116 2.9 164 23.7 21 14.9 69 15.4 117 6.5 165 627.6 22 56.1 '70 27.2 118 13.4 166 54.6 23 12.0 71 11.3 119 4.1 167 54.9 2 A 20.9 72 18.0 120 6.5 168 17.8 25 752.3 73 75.0 121 9.3 169 19.2 2c 57.8 74 750.4 122 12.0 170 12.0 27 66.5 75 27.4 123 5.7 171 12.1 28 23.6 76 75.3 124 6.8 172 8.3 29 18.8 77 193.9 125 36.5 173 7.9 30 10.5 78 789.9 126 460.6 174 5.6 31 18.6 79 66.6 127 10.7 175 5.8 32 11.9 80 153.0 128 35.8 176 4.4 33 7.6 81 4.9 129 11746.1 177 42.2 34 10.7 82 6.8 130 1249.6 178 17.9 10.4 35 83 4.9 131 784.0 179 14.3 36 15.7 84 7.5 132 224.1 180 10.5 37 14.5 85 10.3 133 7896.0 181 19,0 38 22.4 86 19.1 134 549.3 182 10.8 39 21.2 87 14.6 135 550.8 183 10.7 40 35.3 88 60.0 136 116.9 184 7.2 41 41.7 89 18.2 137 98.6 135 8.2 42 27,2 90 27.7 138 65.4 186 6.0 43 81.2 91 19.4 139 82.9 187 5.9 44 44.8 92 18.7 140 328,1 188 4.5 45 26.8 93 34.2 141 28.1 189 4.5 16.4 46 94 61.1 142 16.5 190 3.5 47 48.3 95 22.2 143 37.0 191 3.5 48 26.0 96 34.3 144 15.1 192 2.8 (continued) A27 Appendix IX.— Continued. k = 3.0. 1 .6 49 3.9 97 1.6 145 59.5 2 .8 50 8.1 98 4.4 146 27.9 3 .8 51 7.8 99 .8 147 19.8 4 1.2 52 34.1 100 1.6 148 14.4 5 1.1 53 10.7 101 31.3 149 45.9 6 1.9 54 30.8 102 826.1 150 15.5 7 1.9 55 22.1 103 4.4 151 36.7 8 4.7 56 61.1 104 31.4 152 9.6 9 4.7 57 376.2 105 32.2 153 10.1 10 2.1 5B 38.8 106 6.3 154 15.6 LI 29.2 59 454.7 107 5.1 155 5.6 12 4.9 60 91.9 108 3.2 156 4.4 13 1.5 61 44.5 109 4.9 157 4.1 14 1.0 62 14.9 110 7.9 158 2.2 15 2.5 63 304.3 111 4.6 159 2.3 16 1.5 64 43.3 112 20.0 160 1.4 17 1.2 65 1.5 113 .6 161 11.9 18 2.0 66 2.2 114 .9 162 28.6 19 1.2 67 1.1 115 .4 163 7.5 20 1.9 68 1.6 116 .6 164 6.9 21 4.7 69 3.8 117 1.7 165 608.8 22 29.2 70 8.1 118 4.6 166 26.1 23 2.9 71 2.6 119 .9 167 26.3 24 6.3 72 4.7 120 1.7 168 5.2 25 742.8 73 . 34.1 121 2.5 169 5.6 26 29.7 74 710.6 122 3.6 170 3.0 27 32.2 75 8.1 123 1.3 171 3.0 28 7,0 76 34.1 124 1.6 172 1.8 29 5.7 77 84.5 125 IB.3 173 1.7 30 2.5 78 446.1 126 453.1 174 1.1 31 4.8 79 21.9 127 3.2 175 1.2 32 2.7 SO 60.0 128 18.1 176 .8 33 L. 6 81 1.0 129 11688.7 177 19.8 34 2.4 82 1.5 130 720.2 178 5.1 35 2.3 83 1.0 131 443.4 179 4.0 36 3.9 84 1.9 132 90.9 180 2.5 37 3.4 85 2.5 133 7815.0 181 5.7 38 5.9 86 6.0 134 304.4 182 2.6 39 5.4 87 4.8 135 304.9 183 2.6 40 10.3 88 32.0 136 44.6 184 1.5 41 13.1 89 4.8 137 36.3 185 1.8 42 7.3 90 8.2 138 25.3 186 1.2 43 31.2 91 6.1 139 35.8 187 1.2 44 13.3 92 4.9 140 306.9 188 .9 45 .7.2 93 9.6 141 8.0 189 .8 46 4.0 94 19.4 142 4.1 190 .6 47 14.9 95 5.7 143 15.5 191 .6 48 7.0 96 9.6 144 4.0 192 .5 BIBLIOGRAPHY BIBLIOGRAPHY Alcaly, R. 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