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AIR PASSENGER ROUTES IN HUB AND SPOKE NETWORKS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By Wei Song, M.S., M.A.
*****
The Ohio State University 2000
Dissertation Committee Approved by Professor Morton O'Kelly, Adviser
Professor Lawrence Brown ^ Adviser ^ Professor Randall Jackson Department of Geography UMI Number 9962454
UMI
UMI Microform9962454 Copyright 2000 by Bell & Howell Information and Leaming Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code.
Bell & Howell Information and Leaming Company 300 North Zeeb Road P.O. 80x1346 Ann Arbor, Ml 48106-1346 ABSTRACT
During the decades following airline deregulation,
domestic airline industry has undergone significant structural
and operational change. Major airlines have converted from
linear structures to national hub and spoke network. While many efforts have been made to examine the network point of view of the hub and spoke network, there are still open questions about passenger behavior in hub-based airline networks. This research is focused on the following aspects of the airline hub and spoke network and operations based on aggregate passenger data: (1) routing structure, (2) aggregate market share, (3) price variations, and (4) inconvenience of travel in the hub and spoke network.
The development and expansion of hub and spoke structure are results of the efforts of airlines to pursue network economies. Passenger benefits of hub and spoke route system lie in such service aspects as increased number of city pairs served, more frequent and direct flights from hubs, and ease of online transfers. Diseconomies of travel distance, congestion and delay at hubs, and monopoly air fare are
ii primary unfavorable impacts.
Chi-square based statistical analyses are performed to test the interdependence between routings and city-pair distance, and quantify the strength of the association. It is shown that at an aggregate level, airline markets are strongly dominated by direct routes, especially in short-haul markets.
However, in long haul markets one stop routes could gain a significant passenger share.
Post-deregulation air fare variations are examined in relation to routings and departure aiirports. Large fare disparities are found between long-haul eind short-haul routes, between direct and stopped routes in the markets of similar stage length, between flights departing from different airports, and also between flights departing from the same airport at various times.
A logit aggregate passenger route model is developed to examine the influences of the following route attributes in the hub network: air fare, travel time en route, waiting time at hubs, activity level of hub airport, and the number of stops on a route. Route elasticities in response to air fare and travel time, as well as values of travel time and waiting time are also derived. A highly diversified and complicated route structure is revealed by Atlanta-based routes based on which the logit model is calibrated. While the general
ill preference of passengers for direct routes of low fare and short distance is demonstrated by the consistent estimates of air fare, travel time and number of stops, the impacts of waiting time and hub activity level vary largely. The route elasticities and values of travel time and waiting time also exhibit variations with respect to routing, stage length, and hub airports.
IV Dedicated to my parents and my family ACKNOWLEDGMENTS
I wish to express my heartfelt gratitude to my academic
advisor. Professor Morton O'Kelly, for his untiring guidance,
encouragement, and support. His constructive advice and
invaluable insights guided me throughout my graduate study auid
dissertation research. The completion of this research would not be possible if it wasn't for him. He not only is the best
advisor I have ever had, but also serves as a model of scholar
for his rigidity and devotion to academic work.
I am also grateful for Professor Lawrence Brown,
Professor Randall Jackson and Professor Marc Posner for their comments and suggestions.
I would also like to thank my colleagues in the Geography
Department at University of Wisconsin-Parkside, Professor
Chelvadurai Manogaran, Professor Curtis Richards, and
Professor Richard Walasek, for their ongoing support.
I am also grateful to my friends at The Ohio State
University and at University of Wisconsin-Parkside for their constant encouragement and support.
VI I am greatly indebted to my parents and parents-in-law for their inspiration and unconditional support. Finally, my wife, Yu Liu, provided much needed encouragement, patience and understanding as this research followed a path that contained many detours. Without her continuing support, this research would have become a formidable task.
vu VITA.
October 14, 1963 ...... Born - Harbin, China
1987 B.S. Economic Geography, Peking University, China.
1990 M.S. Urban & Regional Studies, Peking University, China.
1992 M.A. Geography, The George Washington University.
1992 - 1996 ...... Graduate Teaching Associate The Ohio State University.
1996 - 1997 ...... Visiting Adjunct Instructor Department of Geography Florida Atlanta University.
1997 - present ...... Assistant Professor Department of Geography University of Wisconsin-Parkside.
PUBLICATIONS
1. M. E. O'Kelly, W. Song and G. Shen, "New Estimates of Gravitational Attraction by Linear Programming", Geographical Analysis, Vol. 27, No. 4, October 1995.
2. W. Song, "Urban Waste Economy and Its Evolution Model", Urban Economic Research, No. 5, 1989.
viix 3. W . Song, "Urban-Rural Linkage and the Change in Urban Waste Economy in China after 1949", Youth Geographer, Vol.5, No.4, 1989.
4. W. Song and Y. Liu, "Dependent Economy and the Urban Problems in Developing Countries", Urban Problems, No.2, 1989.
5. W. Song, "Trends of the Meaui Center Transferring in Urban Distribution of China", Urban Problems, No. 2, 1988.
FIELDS OF STUDY
Major Field: Transportation Geography
Minor Field: Geographic Information Systems (GIS)
IX TABLE OF CONTENTS
Page Abstract ...... ii
Dedication ...... v
Acknowledgments ...... vi
Vita ...... viii
List of Tables ...... xiii
List of Figures ...... xv
Chapters :
1. Introduction ...... 1
1.1 Hub Network Optimization Analysis ...... 3 1.2 Analysis of the Impact of Hub-and-Spoke Network on Airline Operation ...... 6 1.3 Remarks ...... 10
2. Economies of Hub-and-Spoke Network and Implications ...... 14
2.1 Economies of Airline Hub-and-Spoke Network ...... 14 2.1.1 Economies of Traffic Density ...... 16 2.1.2 Economies of Network Structure ...... 20 2.1.3 Economies of Hub-and-Spoke Network ...... 22 2.2 Air Travel and Convenience Considerations ...... 24 2.2.1 Demand for Scheduled Air Travel ...... 25 2.2.2 Convenience of Scheduled Air Service 28 2.3 Implications of Airline Hub-and-Spoke Network for Air Passengers ...... 32 2.3.1 Passenger Benefits ...... 32 2.3.2 Diseconomies of Route Distance ...... 36 2.3.3 Congestion and Delay at Hubs ...... 37 2.3.4 Airline Hub Dominance auid Monopoly Price ...... 41 2.4 Remarks ...... 42
3. Direct or Stopped Trips: Relationship between Routing and Origin-Destination Distance ...... 45
3.1 Air Passenger Database ...... 46 3.2 Routing by Origin-Destination Distance ...... 49 3.3 A Correspondence Analysis of the Association between Routing and 0-D Distance ...... 55 3.4 Origin Airports : Will They Make a Difference in Routing? ...... 60 3.5 Remarks ...... 71
4. Domestic Airline Pricing Analysis ...... 73
4.1 Air Fare Variations ...... 73 4.2 Air Fares vs. Various Routings ...... 80 4.3 Air Fares and Origin Airports ...... 100 4.4 Remarks ...... 110
5. Aggregate Passenger Route Analysis ...... 112
5.1 Formulation of Passenger Route Analysis - Theoretical Background ...... 114 5.2 Attribute Variables in Passenger Route Analysis ...... 119 5. 3 The Logit Passenger Route Model and Model Calibration ...... 126 5.4 Analysis of Elasticity and Substitution ...... 130 5.4.1 Route Choice Elasticity ...... 131 5.4.2 Marginal Rate of Substitution (MRS)and Value of Time ...... 132 5.5 Summary ...... 134
6. Results of Empirical Passenger Route Analysis ...... 136
6.1 Characteristics of Atlanta-Based Routes ...... 136 6.2 Discussion of Passenger Route Model ..... 174 6.3 Analysis of Elasticity and Marginal Rate of Substitution ...... 183 6.4 Remarks ...... 188
XI 7. Conclusions and Recommendations ...... 192
7.1 Development of Airline Hub-and-Spoke Network and Its Passenger Implications ...... 192 7.2 Air Fare Variations in the Hub-and-Spoke System ...... 195 7.3 Structure of Passenger Routes ...... 197 7.4 Further W o r k ...... 201
Bibliography ...... 204
Appendix A ...... 211
Appendix B ...... 212
XU LIST OF TABLES
Table Page
1.1 FAA Hub Classification ...... 8
2.1 Schedule of Connected Flights Syracuse-Portland 1978 and 1997 ...... 35
2.2 Selected Trips between LaGuardia and Tampa ...... 38
2.3 Peaking of Arrival in Morning at Atlanta Hub (February 1984) 39
3.1 Data Items of the Aggregate Passenger Database ...... 47
3.2 Routing by 0-D Distance ...... 51
3 . 3 Average Itinerary Distance and Air Fare ...... 54
3.4 Airports Ranked by Departure and Arrival of Passengers ...... 61
3.5 Airports Grouped from Crosstabulation Analysis ...... 65
4 .1 Average Air Fares of Selected City/Airport Pairs ...... 79
4.2 Air Fares vs. City/Airport Pair (O-D) Distance ...... 91
4.3 Itinerary Distance vs. City Pair (O-D) Distance ...... 95
4.4 Air Fares vs. Itinerary Distance ...... 99
4.5 Air Travel Data at Nation’s Leading Airports (1997) 100
xiu 4.6 Average Air Fares for Trips from Midway and O'Hare Airports in Chicago ...... 107
4.7 Flight Frequencies and Lowest One-Way Fares from Midway and O'Hare ...... 109
5.1 Summary of Variables in Passenger Route Analysis ...... 125
6.1 Atlanta (ALT)-Based Route Structure ...... 139
6.2 Route Passengers (10%) from Atlanta ...... 142
6.3 Estimated Parameters of Logit Passenger Route Model (Aggregate Data) ...... 176
6.4.1 Estimated Parameters of Logit Passenger Route Model (Combined Data) ...... 177
6.4.2 Estimated Parameters of Logit Passenger Route Model (Combined Data) ...... 177
6.5 Estimated Parameters of Logit Passenger Route Model for Grouped Data ...... 180
6.6 Summary of Estimated Parameters of Logit Passenger Route Model for Individual 0-D Pairs ...... 182
6.7 Route Elasticities for Origin-Destination Markets ...... 184
6.8 Marginal Rates of Substitution between Cost and Time for 0-D Markets ...... 187
6.9 Marginal Rates of Substitution between Cost and Time for Groups of Routes ...... 187
XIV LIST OF FIGURES
Figure Page
2.1 A Hypothetical Transit Network ...... 20
3.1 Routing by O-D Distance (All Trips) ...... 52
3.2 Crosstabulation: Routing by 0-D Distance ...... 59
3.3 Thirty One Major Passenger Airports ...... 62
3.4 Routing by O-D Distance (Origin = Washington National Airport)...... 66
3.5 Routing by O-D Distance (Origin =Boston) ...... 67
3.6 Routing by O-D Distance (Origin = Chicago O'Hare) ...... 68
3.7 Routing by O-D Distance (Origin = Dallas/Fort Worth)...... 69
3.8 Routing by O-D Distance (Origin = Los Angeles) ...... 70
4.1 Fare Per Mile vs. City-Pair Distance, 1978 ..... 75
4.2 Average Fare vs. City Pair (O-D) Distance (All Trips) ...... 76
4.3 Fare Per Mile vs. City-Pair (0-D) Distance (All Trips) ...... 77
4.4 Average Fare vs. City-Pair (0-D) Distance (Direct Trips) ...... 82
4.5 Average Fare vs. City-Pair (0-D) Distance (One Stop Trips) ...... 83
4.6 Average Fare vs. City-Pair (0-D) Distance (Two-and-More Stop Trips) ...... 84
XV 4.7 Fare Per Mile vs. City-Pair (0-D) Distance (Direct Trips) ...... 85
4.8 Fare Per Mile vs. City-Pair (0-D) Distance (One Stop Trips) ...... 86
4.9 Fare Per Mile vs. City-Pair (0-D) Distance (Two-and-More Stop Trips) ...... 87
4.10 Average Fare vs. City-Pair (0-D) Distance (General Trend) ...... 92
4.11 Fare Per Mile vs. City-Pair (0-D) Distance (General Trend) ...... 93
4.12 Fare Per Mile vs. Itinerary Distance (General Trend) ...... 97
4.13 Average Fare vs. Itinerary Distance (General Trend) ...... 98
4.14 Average Fare at Airports ...... 103
4.15 Average Fare Per Itinerary Mile at Airports ...104
6.1 One-Stop Routes from Atlanta (ATL) to Memphis, TN (MEM) and New Orleans (MSY) ...... 145
6.2 One-Stop Routes from Atlanta (ATL) to Cincinnati (CVG) ...... 146
6.3 One-Stop Routes from Atlanta (ATL) to Tampa (TPA) ...... 147
6.4 One-Stop Routes from Atlanta (ATL) to St. Louis (STL) ...... 148
6.5 Routes from Atlanta (ATL) to Pittsburgh (PIT) ...... 149
6.6 Routes from Atlanta (ATL) to Washington Dulles Airport (lAD) ...... 150
6.7 Routes from Atlanta (ATL) to Washington National Airport (DCA) ...... 151
6.8 Routes from Atlanta (ATL) to Cleveland (CLE) ...... 152
XVI 6.9 Routes from Atlanta (ATL) to Baltimore-Washington (BWI) ...... 153
6.10 Routes from Atlanta (ATL) to Detroit (DTW) 154
6.11 Routes from Atlsinta (ATL) to Miami (MIA)...... 155
6.12.1 One-Stop Routes from Atlanta (ATL) to Chicago (ORD/MDW) ...... 156
6.12.2 Two-Stop Routes from Atlanta (ATL) to Chicago O'Hare Airport(ORD) ...... 157
6.13 Routes from Atlanta (ATL) to Philadelphia (PEL) ...... 158
6.14 Routes from Atlanta (ATL) to Houston (lAH/HOÜ) ...... 159
6.15 Routes from Atlanta (ATL) to Kansas City, MO (MCI)...... 160
6.16 One-Stop Routes from Atlanta (ATL) to Dallas (DFW) ...... 161
6.17 Routes from Atlanta (ATL) to Newark, NJ (EWR) ...... 162
6.18 Routes from Atlanta (ATL) to JFK Airport, NY (JFK)...... 163
6.19 Routes from Atlanta (ATL) to LaGuardia Airport, NY (LGA) ...... 164
6.20 Routes from Atlanta (ATL) to Minneapolis (MSP) ...... 165
6.21 Routes from Atlanta (ATL) to Boston (BOS)...... 166
6.22 Routes from Atlanta (ATL) to Denver (DEN)...... 167
6.23 Routes from Atlanta (ATL) to Phoenix (PHX)..... 168
6.24.1 One-Stop Routes from Atlanta (ATL) to Los Angeles (LAX) ...... 169
6.24.2 Two-Stop Routes from Atlanta (ATL) to Los Angeles (LAX) ...... 170
xvu 6.25.1 One-Stop Routes from Atlanta (ATL) to San Francisco (SFO) ...... 171
6.25.2 Two-Stop Routes from Atlanta (ATL) to San Francisco (SFO) ...... 172
6.26 Routes from Atlanta (ATL) to Seattle (SEA) 173
xvm CHAPTER 1
INTRODUCTION
Airline deregulation has led to profound changes in the
structure of the industry. In addition to giving airlines the
freedom to set fares, deregulation removed restrictions on
entry and exit, allowing the carriers to expand, reconfigure
and rationalize their route structures. This flexibility led
in the 1980s to a dramatic expansion of hub-and-spoke networks
to provide efficient linkages between the interacting cities.
Generally, hubs are a type of facility located in a network in
such a manner so as to provide a switching point for flows
between other interacting (non-hub) nodes (Taaffe, Gauthier,
O 'Kelly, 1997, Chapter 13). Practically, these hubs are of
great importance because they are also at the heart of many
express delivery system, truck distribution networks, and also
in the design of advanced communication and computer systems.
Studies of hub facility location and hub network design, as well as the analysis of underlying economic forces, have recently drawn attention from researchers with varied academic backgrounds. A network with hub facilities acting as switching, transshipping or sorting points in the
transportation and telecommunication networks is considered to
be able to use a relatively small number of links or paths to
serve many origin-destination pairs (or demand nodes) . The
rationale or advantages of a hub system lies in the following:
(1) economies of scale due to flow concentration on a small
number of links; these economies derive from the lower unit
operating costs of larger aircraft, as well as the benefits to
passengers of increased service frequency; (2) very likely
smaller overall investment in the network implementation; (3)
the powerful flow (traffic, data, etc.) control and management
functions performed by the hub facilities. The disadvantages
of hub systems may lie in: (1) relatively large fixed and
transshipment investments or costs in the hub; (2) longer
distance or time required for movements via hubs between a
specific origin and destination. The second issue of the disadvantages is more important for time-sensitive flows, such
as airline passenger flows. All these factors have quite
significant impacts on the economies and profitability and dominance, as well as the spatial organization of airlines
(Campbell 1994).
Two general approaches of hub-related research have emerged, each of which is concentrated on different aspects of the economic and spatial organization of the transportation network.
1.1 Hub Network Optimization Analysis
The first approach involves optimization analysis on the hub network, in which hub location and network design have been the dominant topics. Two basic tasks are involved. The first is to locate a certain number of hubs out of a known set of potential hub locations. The backbone could connect these locations in a variety of ways. The second task is then to allocate non-hub origins and destinations to the hubs which have been located in the first task. Usually, a set of origins, destinations, potential hub candidates, and a network connecting these points or nodes must be specified, as well as the traffic data, such as 0-D matrices of flows, distances, costs, and so on (Campbell, 1994).
A hub system can be viewed as consisting of two levels of network: (1) a hub level network, sometimes called the backbone network in telecommunication, connects the hub facilities, corresponding to the first task of hub network design; (2) an access level (spoke) network then connects demand points to the hub level network (corresponding to the allocation task) . Economies of scale, one of the most important advantages of hub-and-spoke network are generally considered to be closely related to the hub level network (Hansen, 1990). Activities in the hub level network are
usually discounted (i.e. the reduced cost or time per unit of
flow) due to the greater concentration of flow in the hub
level network. Basically, this is the place where the economic benefits are incorporated explicitly in the hub network design
research. The usual practice is that a constant a (Of costs between hub-to-hub transhipment in the hub level network (O'Kelly, 1986). A general picture of transportation-related hub facility location and network design is given by O'Kelly and Miller (1994), and Campbell (1994) in their review and synthesis of hub network design problems. Varied formulations of hub location/allocation problem under different hub network topology have been developed, based on the different assumptions about the node-hub assignment, internodal connection and the inter-hub connectivity. Generally, in these studies to locate hubs and to allocate the demand points, the objective function to be minimized is the total transportation costs, including the transshipment cost from non-hub demand node to hub, (non-hub to non-hub points, if possible), transshipment cost between hubs, as well as a fixed cost for the hub locations. The objectives and constraints in hub location and network design research correspond to those in central facility (single) or facilities (multiple) location problems. Therefore, the fundamental hub location and design problems have strong facility location analog. Like other location problems, most hub location and network design problems are difficult to solve. Therefore, lots of effort has been spent on the mathematical formulations, the choice of decision variable, the choice and structure of different mathematical programming (i.e. general linear programming, mixed integer programming, quadratic programming) as well as the pursuit of efficient algorithms in solving these fomnulations. Varied heuristic techniques are used in the research. Some heuristics address both the locations of hubs and the allocation of demauid points to hubs, while others assume a particular scheme, such as allocating all demand points to the nearest hubs. In contrast to the above total-cost-minimizing hub location-allocation analysis, other objectives in the optimal hub network have also been addressed. For instance, studies have included fixed costs analysis for hubs (O'Kelly, 1992a). Hall (1989) considers the impact of time restrictions and time zones on overnight package hub networks. The relationships between package arrival rate, sorting rate and hub network design are explored. Hub locations to minimize the maximum travel time on the network are analyzed by Iyer and Ratliff (1990). In this study, they discuss centralized sorting system (CSS) and decentralized sorting system (DSS), and provide a polynomial time algorithm that locates a given number of local centers, allocates customers to local centers so that the DDS time guarantee is minimized. The impacts of congestion in the determination of hub locations have also been addressed. O'Kelly (1986b) analyzes the measures of hub usage in terms of the number of arrivals and departures. Grove suid O*Kelly (1986) explore congestion in hub and spoke networks by simulating daily operations. A single hub is obtained from 25 demand points with minimum level of congestion. Considering the importance of time factor, optimal scheduled ground time for flights at hub airports is examined by Trietsch (1993) , with the objective being to minimize the expected sum of costs and penalties. 1.2 Analysis of the Impact of Hub-and-Spoke Network on Airline Operation The second approach primarily deals with the examination of basic economic aspects of the hub-and-spoke network, such as economies of scale, and other economic factors related to the freight consolidation and concentration. In these studies, airline hubs practically refer to those key airports around which the entire route system is organized. Airline hubs emphasize the transfer of passengers, and accordingly, at these hubs, the airlines’ offer a large number of connecting flights to other areas or destinations. In previous work, the U.S. General Accounting Office (GAO) assumed a concentrated hub to be an airport which was one of the 75 busiest in the nation in terms of enp 1 anements and where one carrier accounted for at least 60 percent of enplanements or two carriers combined accounted for at least 85 percent. The air traffic hub structure developed by the EAA is also widely used in the studies. Social, economic and other factors influencing a community’ s ability to generate air carrier traffic are brought together in this structure. The FAA air traffic hubs are not airports per se. Rather, they are the cities and Standard Metropolitan Statistical Areas (SMSAs) requiring aviation services. These communities fall into a four hub classification scheme, depending upon the community's percentage of the total enplaned passengers in all services and operations of U.S. certificated route air carriers within the 50 States and other designated areas. The FAA hub classes are described in Table 1.1. The appearance and development of hub-and-spoke network, or airline hubbing, has its roots in the economic benefits brought by a structure in which a few airports are used as collection and distribution centers for the passengers. FAA Hub Type Percentage of total US passenger enplanements Large 1.00% or more Medium 0.25 to 1.00% Small 0.05 to 0.25% Nonhub less than 0.05% Table 1.1 FAA Hub Classification By consolidating passengers through a few selected airport hubs, an airline takes the advantage of the resulting higher volume by using large, relatively efficient aircraft eind can raise the frequency of service it offers passengers, while the passengers benefit from the economies of the increased frequency (Kanafani and Ghobrial, 1985). Generally speaking, economies of scale, corresponding to the consolidation of freight at hubs play an critical role in this process. The determination of airfares in individual city-pair markets in response to airline deregulation has been an important research topic. This line of research contains contributions by Bailey, Graham and Kaplan (1985), Berry (1990), Borenstein (1989), Call and Keeler (1985), Graham, Kaplan and Sibley (1983), Hurdle et al. (1989) and Morrison and Winston (1989, 1990) . These studies typically explore the 8 connection between airfares emd market-specific measures of demand (city population and income levels, tourism potential), cost (flight distance, load factors) , and competition (number of competitors, market share). Considering the critical role played by the network in lowering airline costs, network variables reflecting the structure of airline hub-and-spoke networks are included in many analyses, in addition to only market-specific variables, to show their effects on the airline costs. Varied measures of the degree of hubbing are derived, which include, among others, the proportion of each airline's total departures leaving from its hub(s), the percent of total departures carried from the N airports with the greatest number of departures, or the proportion of an airline's total departures leaving from the three percent most utilized airports in that airline's network (Kanafani and Hansen, 1985). These measures are designed to reflect the characteristics or the level of concentration of existing hub-and-spoke structure. Toh and Higgins (1985) find a positive relationship between the extent of hubbing and airline profitability. McShan and Windle (1989) argue that hub-and-spoke routing increased by 48 percent between 1977 and 1984, while airline costs are reduced by .1 percent for every 1 percent increase in hub-and-spoke routing. By linking airline fares to the structure of hub-and-spoke networks, Brueckner, Dyer and Spiller (1992) provide evidence of the importance of networks in reducing airline costs. More generally, the forces leading to higher traffic densities on the spokes of a network are considered to reduce fares in the markets it serves. While airline hubbing is considered primarily a strategy to increase airline network efficiency and to reduce operating costs, hub-and-spoke structure is also viewed as a marketing strategy that allows individual airlines to achieve dominant market shares at their hub airports and to take advantage of market preferences for the increased frequencies that the strategy permits (Hansen and Kanafani, 1989) . Borenstein (1989) then finds that increased market dominance at a hub resulted in some degree of market power (i.e. higher fares) . A recent study conducted by USA TODAY has found that hub fliers pay an average 22% more on each ticket than non-hub fliers (Rosato and Overberg, 1998). 1.3 Remarks The study of hub-and-spoke network is a very broad and complicated field. The general approaches reviewed above are the two most important fields trying to understand, explain and design the air transportation network from different perspectives, although they do not cover all the hub-related research efforts. The first approach tries to optimally design 10 the structure of the network, in terms of the huh locations, allocation of demand nodes and spatial interaction patterns, while the second approach tends to explore the impact of hubbing network characteristics on airlines’ operations, reflected in costs and fares. From the major body of optimization analysis on hub location and network, many potential issues remain to be further explored. Much of the transportation-related hub location studies has concentrated on the basic p-hub median problems. Research on varied other hub location problems, such as the degree of discount for hub level transportation, the number of communicating 0-D pairs, the level of fixed costs and cost allocation, etc., is only at the beginning stage. Meanwhile, the general objective to minimize the total transportation cost in the analysis emphasizes much on the service supply side, or airlines, with little attention being paid to consumers or airline passengers. Theoretically, the inconvenient routes of hub network with one or more transfers could lead to the loss of customers to other competitors providing better services. An apparent question then, is how can an airline provide varied incentives for passengers to chose its service under hub-and-spoke organization. Probably, there will be no single answer to this question, eind the answers may be quite complicated. 11 Similar problems also remain in the second approach. The economic analysis of the impacts of hub-and-spoke network on the airline operations is made from the point of view of service supply side. Several researchers have dealt with the route choice model (Kanafani and Ghobrial, 1985), market share model (Hansen, 1990), and the role of price in route (connecting or nonstop) development (Schwieterman, 1990) . However, the explicit analysis of the process and factors influencing the travel (route) choice of airline customers has received relatively little attention. Meanwhile although it has been argued that the resulting economies of hub-and-spoke network enjoyed by airlines are related to the concentration and consolidation of traffic on the network, the detailed and systematic analysis of those economies have not been performed. The nature, format and extent of these economic benefits need more explicit examination. Moreover, it has been widely mentioned implicitly in many studies that the economies resulting from hub-and-spoke structure will at the same time benefit passengers traveling on the network via hub airports. However, little effort has been made to explicitly link these network economies to the passengers' travel choices. The effects of airline operation factors, as well as the characteristics of hub networks need to be addressed. It is necessary to build a research framework under which 12 the economic analysis of hub-and-spoke network analysis and the travel (route) choice of passengers could be closely related. The pursuit of the impacts of the factors on the passenger travel choice will further enhance our understanding of the nature, operations, and influences of hub-and-spoke network itself, and provide us with more insights into the system and the responses of passengers to it. This will in turn be very important in the network design to meet varied optimization objectives. In the following chapters, economies of hub-and-spoke network and their implications for air carriers and air passengers will be addressed. A correspondence analysis will also be conducted to examine the association between different air routings and one of the most important factors in air travel - city-pair or origin-destination distance. An analysis of airline pricing will also be conducted, in which the general trends of airline aggregate pricing data and variations within these data are investigated. Following these analyses, a logit-based aggregate air passenger route model will be developed and calibrated. The intention is to investigate the aggregate route structure in the hub and spoke network, and reveal empirically the impacts of various route attributes. 13 CHAPTER 2 ECONOMIES OF HUB-AND-SPOKE NETWORK AND IMPLICATIONS This chapter is set out in four sections smd addresses the economic forces behind the airline hub-and-spoke network. In the first section, various network economies in airline operation, as well as their implications to air carriers will be discussed. The nature of demand for scheduled air travel and convenience consideration in air travel will be discussed in the second section. The implications of airline hubbing to passengers will then be addressed in the third section, following which there will be some concluding remarks. 2.1 Economies of Airline Hub-amd-Spoke Network The economies of hub-and-spoke structure are analyzed in comparison to the airline route structure prior to deregulation. In the past, there was constant pressure (from communities and from the CAB) for more and more direct, point- to-point non-stops. Many city-pair markets, however, could not support nonstop service in terms of their own 14 origin/destination traffic. Economic viability frequently depended on adding "traffic flows" from back-up markets on either end of a nonstop route. So, cities often were added to a carrier's route system specifically for the purpose of providing enough traffic to make nonstop service viable. For example, the traffic from Dayton to Los Angeles had previously helped TWA support nonstop service from Indianapolis to Los Angeles. Because of the protection afforded by a regulated route franchise system, the back-up markets from some nonstop routes could be expected to remain relatively stable over long periods of time. In this framework, the airline route structure evolved gradually into many "linear" patterns, in which one city would mainly serve as back-up to some specific route segment, while other cities would back up other routes, etc. With deregulation, several important changes took place. First, carriers could no longer regard their back-up traffic markets as stable or "secure". Second, with pricing freedom, traffic can now be diverted from long-haul nonstops, by carriers who serve the same city-pairs on a one-stop or connecting basis, and who can offer "fill-up" discounts below normal nonstop fares. Third, as a mirror image of the preceding point, the effort to combat the diversionary effect of the one-stop discounts sometimes has led the nonstop 15 carriers to come close to matching those discounts on their nonstop flights. While this has retained the traffic^ it does so at a lesser revenue yield, and thus still ends up impairing the viability of the long-haul service (Brenner, Leet and Schott, 1985). The combination of these factors has forced carriers to shift their primary emphasis from point-to-point nonstop service to hub-and-spoke patterns. By merging many spokes into a hub, and thereby exchanging traffic on a connecting basis between a wide variety of origins and a wide variety of destinations, carriers are no longer so vulnerable to the diversion that might occur on one particular traffic flow routing. The expansion of airline hub-and-spoke configuration thus is a response of carriers to a new market environment. They are trying to reap the benefit of the economies resulting from the new routing system to develop their competitive advantages. Analysis of transportation economics shows that certain types of economies may exist corresponding to the organization of transportation network. They can be used to explain the rationale of airline hubbing. 2.1.1 Economies of Traffic Density This first type of economy is known as "economies of traffic density", which is the result of a transportation service supplier's ability to cluster passenger for further 16 transport, thus lowering the operational cost per unit output. This could also be defined as the decline in the unit cost resulting from carrying more passengers over a given transport network (e.g., airline or rail route network) (Berechman, 1993) . If the cost per passenger for the air carrier is lower on larger aircraft than on small ones, it can economize by first collecting all passengers from various origin locations, then funnelling and recombining them at a hub, to be transported to their final destination afterwards. In hub-and- spoke routing system, passengers with the same origin but different destinations (or vice versa) are consolidated on the same route (spoke) . This consolidation offers the airlines two potential cost advantages over alternative route structures. First, the consolidation of passengers from many routes on a single spoke increases the density of service along each spoke. Although hubbing need not change which airports an airline serves nor the number of passengers it transports, hubbing does decrease the number of routes with a resultant higher passenger density on each. By concentrating resources into this configuration, airlines are able, through the better utilization of their aircraft and flight crews, to derive considerable economies of traffic density and lower their operating costs. Given the size and layout of the network, a proportional increase in the number of passengers transported 17 will result in a less than proportional increase in total costs. Second, the potential increase in frequency along each spoke may increase demand (i.e. revenue passenger miles) and therefore density. Previous evidence on returns to density in the airline industry indicates that increased density lowers unit costs (Caves, Christensen and Tretheway, 1981). The carriage of passengers with different origins or destinations on the same aircraft is also sometimes referred as economies of scope. It is indicated that it could result in 5-10% higher load factors on routes radiating from a hub (Williams, 1994) . Generally, the capability of transit supplier to reduce unit cost by bunching and redirecting passengers is due to three major factors: the ability to use large aircraft; the ability to employ aircraft at high frequency; and the access to a central large facility (e.g., a hub). Obviously, there are limits to aircraft size and frequency of use, which curtails the extent of traffic density economies. The third factor is considered to be the key one as it affects the supplier's cost of service provision as well as the structure of its network (Berechman, 1993) . This third factor, in fact, also closely relates to the determination of airline hub location to gain a superior position in the provision of services to achieve the savings. The airline hubbing provides the most efficient way of 18 overcoming the production indivisibilities inherent in the use of large aircraft. The relationship between aircraft technology and network structure is analyzed by Kanafani (1981). He found that...”... connectivity decreases with aircraft size and with a 140 seat aircraft the network reaches nearly a hub and spoke system”. This aircraft carrying capacity is the largest he considered^ and it clearly shows that technically the best means of overcoming the indivisibilities is to develop route systems based on the hub- and-spoke principle. It becomes easier for operators with large aircraft to compete even in less dense markets, as raising the number of destinations served from a traffic hub enables them to increase the size of aircraft and/or serve frequency that could be operated to any one point in a network. Work conducted at the University of Illinois showed that in 1985 a major carrier such as Delta out of Atlanta carried about 36,000 passengers per quarter on an average spoke route, a medium size carrier such as US Air out of Pittsburgh carried 24,000, and a low density network such as Ozark out of St. Louis carried about 12,000 passengers. The comparable marginal costs for each additional passenger flown are $107, $113, and $134 respectively - 25 percent higher to carry an extra passenger on a low density route (Button and Stough, 1998) . 19 2.1.2 Economies of Network Structure The economies of hub-and-spoke structure lie not only in advantages from the consolidation of passengers on each route (or spoke) and the return to scale at the hub airports, but also in the ability of airlines of rerouting, reorienting and coordinating the traffic on the scale of whole airline network. The economic advantages which relate to the whole network layout, is termed "economies of network structure". These economies refer to the savings in the cost of service production due to cost advantages arising from the production of similar services on different routes belonging to the same network (Berechman, 1993). For the transportation firm, the multi-output is corresponding to the provision of multiple services at a location in the network. This will be the case, for example, when the transit firm is able to swap labor or Figure 2.1 A Hypothetical Transit Network 2 0 equipment between routes/ either because of the particular layout of the network (e.g. / in a hub-and-spoke configuration), or because of operational conditions on each route (e.g./ when services are operated at different frequencies so that the firm can use vehicles between runs on one route for operation on another) . One demonstration of this type of economy is based on a hypothetical transit network represented by Figure 2.1. If peak demand on the A-C route does not coincide with that on the B-C route, the transit firm can utilize its resources more efficiently by rerouting vehicles that have reached destination C from origin A, after providing peak-demand services on the A-C route, to transport passengers from C to B. Under these circumstances, marginal costs of service provision on the A-C route will be lowered when more output is produced on the B-C route which links into the same hub facility at C. Economies of network structure imply increasing returns to the network structure and size, which reflects the cost complementarity between different routes in that an increase in the level of service a route i could cause a decline in the marginal cost of a service of route j (Berechman, 1993). Hub-and-spoke network enables airlines to make on-line transfers instead of interline transfers which are less preferred by passengers. This provides airlines with greater 21 potential to manipulate and schedule its traffic from the point of view of the whole network, which will in turn significantly enhstnce the overall performauice and operations of the airline. Aircraft arrivals and departures at the hub facility could be judiciously synchronized so that passengers brought in from the outlying cities can be reorganized and transferred to the airline's other flights departing from the hub. There are, of course, limits of economies of network due to technical factors such as the finite capacity of a hub airport or of the air corridor in which planes are flying. 2.1.3 Economies of Hub-and-Spoke Network Essentially, economies of density and network structure are associated with the broad ideas of economies of scope and economies of scale which have been heavily researched in studies on the hub-and-spoke network. They are also closely related with respect to their economic source, as well as with respect to their effect on the structure of the route network, as means to reduce costs and enhance the system performance. The fundamental economic factor which gives rise to these economies of production (or service) is the joint production of spatially differentiated transport services. Economic literature indicates that there exist economies of scope associated with producing multiple products which (a) 2 2 share a fixed input factor or, (b) are connected with a network of operations of one sort or another (e.g., communications, information or transportation network). Air transport is a production process with each route in the airline's network representing a different product. Joint production arises when the carrier uses a common or a shared major input factor, such as an airport terminal, in this process. The joint-product nature of the production process implies that there exists an association or interdependence between potentially all routes in the airline network system. The production of flight services on one route influences production of services on other routes by generating increased passengers between routes. Because there is a large scope of operations, economies of specialized inputs, such as airplanes, can be reaped (Reynolds-Feighan, 1992). The concept of joint production implies that the total cost of producing a set of outputs by a centralized coordinating firm is less than the sum of the cost of production of each separate output by individual firms. In the airline network context, joint production implies that C( I y^) < I C(yj (2.1) i-l i-1 where C is the cost, y^ can be regarded as the service 23 provided in route (market) i and N denotes the number of routes in the network. An airline hub is the place which consolidates formerly diffused flows by rerouting them and coordinating all routes at the hub. The degree of cost efficiency increases with the number of routes which are incorporated to the same hub facility and the traffic density on each route. For these economies of network to endure it is assumed that if the size of this fixed facility increases, the cost of providing services on each route would decline or, at most, remain constant. Thus, economies of hub-and-spoke network have their extent or threshold beyond which extra costs will be incurred. Putting exact figures on the various economies is difficult. Early analysis indicated, for instance, that due to economies of density, a 1 percent rise in the number of passengers an airline carried resulted in a 0.8 percent reduction in unit cost, and some analysis indicates savings could be higher (Button and Stough, 1998). 2.2 Air Travel and Convenience Considerations The realization of the network economies cannot be reached solely by the efforts of airlines or the suppliers of air services. Instead, in well-functioning competitive markets the desirable rate and quality of production (airline 24 services) are determined directly by the interplay of supply and demand. Sometimes, certain regulations may also be interposed between producers and consumers to influence the pure supply-demand interaction. This situation was especially true in the period prior to deregulation. The travel behavior of air passengers and their response to the newly developed hub-and-spoke configuration is a very important component in the comprehensive study of this important economic phenomenon. An understanding of the nature of demand for scheduled air travel will serve this purpose, which will also lay a background for the discussion of the implications of hub-and- spoke network on air passengers. 2-2.1 Demand for Scheduled Air Travel Underlying many analyses of the demand for air travel is an important assumption that air travel is generally an “intermediate good". It is simply a means of moving from one point to another point for some purpose. Thus, while the Sunday afternoon drive and the cruise represent examples of the consumption aspect of the travel in which people enjoy the travel itself, the trip by commercial airlines is not usually thought of in and of itself as consumption (Douglas and Miller, 1974). By characterizing air travel as an intermediate good, it is possible to hypothesize a decision process 25 followed by travelers from which many important characteristics of air travel demand can be derived. The decision-making of travelers can be considered conveniently to include four basic components. Generally people need to decide (1) whether to travel at all, or how often; (2) by what mode: air, auto, bus, or other (for air travel itself, by which way: direct, nonstop, one-stop, or multiple-stop trip) ; (3) at what time, and (4) by what types of constraints (which may include timing, monetary, and others) . Once the decision to travel has been made, the individual's choice of mode and timing is assumed to be based on the desire to minimize the cost and inconvenience of the travel, or to maximize his welfare under a series of constraints. A full cost of travel may include price or monetary cost, trip time, the schedule convenience, as well as additional factors related to the overall quality of travel services. Normally in the process of pursuing a higher utility or welfare, travelers have to make certain trade-offs in terms of which types of the “costs" are weighted more and need to be minimized. Air travel has typically had a time advantage over other travel modes on the surface, but a slight fare disadvantage, the value of time is an important determination of the choices of travelers between air and other modes (Douglas and Miller, 1974; Viton, 1982; Schwieterman, 1990) . Similarly, air travel 26 demand appears to have higher income elasticity. Generally, a positive relationship between an individual's income level and the value he places on his time is expected. The relative advantage of air travel normally increases with distance over other modes, because the time saving of air travel over surface alternatives grows faster than the money saving of other modes over air. This type of "economies of distance" is an important factor in passengers' route choice (e.g., nonstop vs. stopped trips). It seems that there is a “break point" at which people will find a certain type of trip more economical. The reductions in the air fare usually cause a corresponding reduction in the critical time value. This could enhance the relative level of demand and lead to a greater number of people traveling by air. This explains why more airlines are using discounted fare as a market strategy to enhance their competitiveness and expand their market penetration. Time en route by air normally decreases as aircraft speed increases, or as numbers of stops en route is reduced, or both. So, the reduction of the time en route itself will have the effect of enhancing the relative advantages of air trip and increasing air travel in general. People would have a natural tendency for the short-time trips with few stops. 27 2.2.2 Convenience of Scheduled Air Service While the role of price and time en route is commonly recognized in the examination of air travel demand, another and often overlooked factor of considerable importance is the convenience of scheduled air services. This is especially true under current circumstances with more and more stopped trips in the hub-and-spoke network. In transportation, the timing of the service usually affects its desirability to the traveler. It is directly related to the airline schedule and arrangement of connections between flights at varied stops on the network. Typically, each traveler has some preferred time of departure or arrival for a specific trip. For a traveler, a convenient schedule is one where a departure with an available seat coincides with his most preferred time of departure, or the one where his arrival at a transfer point has a perfect connection with next available flight to the destination with less delay involved at the airport. Conversely, an inconvenient schedule is one which causes him to depart at a less preferred time, or a misconnection at the connecting airport. It is quite likely that the inconvenience of an early or later departure or possibility of misconnection in the transfer would cause the traveler to revise his plans and travel by other routes, modes, or perhaps even not take the trip at all. So, an air passenger considers not only time 28 spent on the plane but also the time spent at the airports (including possible transfer) to catch the available departing flight. This again implies that people prefer a direct or nonstop trip because it has not only relatively shorter time en route but also few transfers or connections which could mean more waiting time and delay. With other factors fixed, direct or nonstop trips would gain a bigger air market share conç>ared with other stopped trips in a given city-pair market. In the real world of air transportation, it is unlikely for every individual passenger to always have the most convenient and preferred trip because there may be many others who prefer the same or similar schedule. For a given day in a particular city-pair market the most preferred departure times of potential travelers vary from morning to late at night. While many travelers' preferences are similar, many or most travelers may be unable to depart precisely at their most preferred time. So, travelers are obliged to adjust their departure time to the closest scheduled flight with an available seat. For air transportation with a quite precise and tight schedule system, travelers usually must make adjustments. Since for the individual traveler the convenience of the service is indicated by the difference between his preferred departure time and his actual departure time, the overall convenience of service in a market is the average of 29 this time difference overall all passengers, that is, the adjustment per passenger. This difference has thus been defined as schadule dalay (Douglas and Miller, 1974) . The expected schedule delay per passenger is then a measurable surrogate for schedule convenience in the market. This aspect of convenience is closely related to the frequency of flights serving a specific origin-destination route. Moreover, it is dependent upon how these flights are scheduled and the time pattern of demand that prevails in the market. In air transport, the level of schedule delay may vary considerably. Generally, a heavily traveled market is likely to have relatively lower schedule delays thsin a more lightly traveled market, since the former have more frequently scheduled departures. Other factors besides daily flight frequencies, such as the availability of seats, also affect schedule delays. Schedule delay can be further split into two basic components - frequency delay and stochastic delay. Frequency de lay is the expected differential for a passenger between the most desired departure time and that of the closest scheduled departure. Meanwhile, if the most-preferred scheduled flight is filled, the traveler will have to take a later or earlier flight. The corresponding stochastic delay then is defined as the expected length of delay a potential passenger faces 30 because of the chance that his most preferred scheduled departure will be booked up and he will have to select another and possibly even a third or fourth, and so on. Generally as the average number of passengers on each flight approaches the average capacity of the aircraft, the probability of not obtaining a seat grows large, and the expected delay grows long. Holding fixed the number of passengers per day desiring service between two points, frequency delays can be reduced by providing more frequent departures, while stochastic delays can be lowered by using larger aircraft or increasing the ratio of seats available to expected passengers (Viton, 1982) . Usually, it is necessary for travelers to make a trade off between convenience and money, where a traveler's value of time could play a crucial role. Different people with varied backgrounds may behave quite differently since they are unlikely to value their time in an identical way. At one end of the scale some high-salaried executives may avoid schedule delay entirely by using a personal or business airplane or pay higher fare to minimize the effect of delay, while at the other end of the scale general passengers have to accept substantial inconvenience in order to cut the fare. 31 2.3 Implications of Airline Hub-and-Spoke Network for Air Passengers Hiib-and-spoke route system is a marketing tool of air carriers to lower operation costs and enhance competitiveness, taking advantage of various network economies. It is, however, a mixed blessing for air travelers. The pros and cons airline hubbing are going to be outlined, considering those basic passenger variables, such as air fare, trip time, schedule convenience, as well as the trade-offs among them. 2.3.1 Passenger Benefits Benefits of hub-and-spoke route system to air travelers can be reflected in such service aspects as increased number of city pairs served, more frequent flights, more direct flights, more opportunities for same day return flights, ease of online transfers, etc.. Hub systems have a multiplier effect on the number of origins and destinations a carrier serves. A vast number of city pairs have been served by connecting flights via hubs, many of which had only limited or no service in the past when regulatory pressure was for direct, nonstop service. Service improvement for 'spoke' city pairs has been great in the newly created hubs. As an example, in 1978, Charlotte received only a single daily nonstop from Boston before it became a hub of 32 us Airways; now it receives more than ten. In 1978, Charlotte had no nonstops from Dallas, now it has more than ten, too. Each major airline develops hub facilities at strategic points in its service networks to reroute passengers from many origins, via hub, to desired destinations. Thus those cities with hub operations become centers of very high concentration of passenger traffic and flight frequencies. Cities such as Chicago, Dallas, Atlanta, Washington, D.C., experienced large increases in flight frequencies. The increased number of flight departures has meant more frequent nonstop service from hubs to more different destinations - a direct improvement in hub accessibility. For travelers living in such cities, there are more air travel options to choose from. Frequent flyer programs are popular now and most regular business travelers are members of at least one program. The value of frequent flyer mileage is greatest for residents of a city that serves as the hub for a large hub-and-spoke network because it translates into convenient free travel to a multitude of destinations. In the decade after the 1978 deregulation and largely as a result of the hub-and-spoke system, the number of passenger enplanements rose by 55 percent to over 140 million per annum. The hub cities themselves have also benefited from increase in business activity attributable to the availability of frequent and extensive air service. It is indicated by 33 Button and Stough (1998) that an airport hub is a stimulus for high-technology jobs to grow in the region. Generally, passengers much prefer single-carrier service over having to change airlines in midjoumey. Because of the coordination of traffic on the whole network, hub-and-spoke networks increase airlines' ability to offer single-carrier service to connecting passengers. Connections of flights at hubs tend to be online instead of interline which saves transferring time including walking time between gates. To illustrate. Table 2.1 shows the service published in the Official Airline Guide (GAG) for the Syracuse-Portland (Oregon) market in 1978 and 1997. In 197 8, there were no carriers serving Syracuse and also Portland. The only available connections were interline involving American Airline and United Airlines, as a result of which connecting lay-over time was quite long. The shortest elapsed time was 8 hours 5 minutes, and the longest was 11 hours 27 minutes. In comparison, direct online connections in 1997 has an average elapsed time of 7 hours 33 minutes - about an hour faster than average time in 1978. Considering the scheduled delay which measures the difference between a passenger's preferred departure time and the actual departure time of the available flight, passengers traveling from hub airports will generally have more choices and opportunities to meet their preference 34 Carriers Departure Arrival Total Type of Time Time Elapsed Service Time December 1978 AA/UA 7:15AM 12:20PM 8:05 Connection AA/UA 9:15AM 3:20PM 9:05 Connection AA/UA 4:06PM 8:55PM 7:49 Connection AA/UA 6:25PM 2:52AM 11:27 Connection December 1997 UA/UA 7:30AM 12:03PM 7:33 Connection UA/UA 10:05AM 3:12PM 8:07 Connection UA/UA 10:30AM 2:36PM 7:06 Connection UA/UA 5:20PM 9:49PM 7:29 Connection Sources : Brenner et al. 1985; Official Airline Guide. Table 2.1 Schedule of Connected Flights Syracuse-Portland, 1978 and 1997 35 in time, thus reduce their schedule delay time. So, in terms of the cost of inconvenience of travel, the hub-and-spoke structure does provide economic advantages not only to the air service suppliers but also to the passengers travelingfrom hubs. These passenger benefits, however, are not evenly distributed over all the routes and over all the passengers. The routes with higher density of traffic generally obtain more benefits compared with the other less-dense routes or spokes. In spite of the positive effects of the hub-and-spoke structure, it is also noticed that the clustering of passengers on the routes and hubs for the purpose of reducing airlines' average costs, has unfavorable impacts on passengers' welfare in varied ways. 2.3.2 Diseconomies of Route Distance Compared with point-to-point service, designing and serving a partially connected hub-and-spoke network has significantly increased circuity in air travel, thereby implying prolonged travel distance and travel time. This effect could be termed as "diseconomies of route distance" in hub-and-spoke system (Weidner, 1995) . Many passengers who do not begin or end their trip in a hub airport have to fly more miles to get to their destination than before deregulation. 36 with estimates of this effect ranging from 4 percent to 30 percent for the average trip (Dempsey and Goetz, 1992) . Table 2.2 compares various routes in the LaGuardia-Tampa market, showing that travel distances of one stop trips are significantly longer than the 1,011-mile nonstop distance. For example, routed through United Airlines' hub at Chicago, the trip consumes 1,745 miles, or 73 percent more miles. The travel distauice of the LaGuardia-Dallas-Tampa route is almost 130 percent longer. It is expected that the itinerary distance would be quite long for many trips involving two or more stops. The excessive increase in overall route length will push passengers tolerance of superfluous in-flight trip time. Meanwhile, this may also add to operating costs of airlines in a way to counter the positive effects of network economies of traffic density, as a result of which consumers would pay more. As indicated in Table 2.2, excluding the LaGuardia- Dallas-Tampa fare which is extremely high, the average fare of one stop trips is about 16 percent higher than the nonstop fare in this market. 2.3.3 Congestion and Delay at Hubs Diseconomies exist at the hub nodes when capacity limitations of the airports are reached and when overall airport size becomes obstructive to flight connections. The 37 Type of Transfer Hub of Distance Average Service Airport (mile) Fare ( $) nonstop n/a n/a 1,011 117.06 Atlcuita Delta 1,166 149.00 Chicago United 1,745 148.24 1-stop Dallas American 2,318 845.07 Detroit Northwest 1,484 120.10 Pittsburgh USAir 1,208 125.48 (Source: DOT 10% 0-D Passenger Survey) Table 2.2 Selected Trips Between LaGuardia and Tampa very nature of hub-and-spoke system requires that airlines concentrate as many incoming and outgoing flights in as narrow a window of time and space as possible in order to maximize the total number of city-pair combinations that can be effectively served through the hub. Thus, a highly congested wave of nearly simultaneous arrivals and departures of a lot of flights would be unavoidable, especially in the time of peak hours during the day. This has contributed to severe problems of congestion and delay at hub airports, especially at large hub airports. Table 2.3 shows an example of peaking of arrivals in morning at Atlanta hub in February 1984. 38 Scheduled Number of Scheduled Arrivals Arrival Time (AM) Delta Other Airlines Total 8:20 4 6 10 8:21 1 - 1 8:22 - 1 1 8:23 3 2 5 8:24 1 2 3 8:25 3 9 12 8:26 2 - 2 8:27 - 1 1 8:28 - 2 2 8:29 2 - 2 8:30 4 4 8 8:31 3 - 3 8:32 - - - 8:33 2 - 2 8:34 1 - 1 8:35 4 4 8 8:36 - - - 8:37 1 - 1 8:38 1 - 1 8:39 3 - 3 8:40 6 2 8 Total 41 34 74 Source: Brenner, Leet, and Schott, 1985. Table 2.3 Peaking of Arrival in Morning at Atlanta Hub, (February 1984) 39 Within a span of 21 minutes between 8:20 AM and 8:40 AM, Delta had 41 flights scheduled to arrive (an average of 2 arrivals per minute) . In addition, during the same period other airlines serving Atlanta scheduled 33 more arrivals, for a total of 74 arrivals in 21 minutes, which is equal to an average of 3.5 arrivals per minute. Facilities and personnel must be geared to handle this type of intense peaking. Moreover, congestion and delays become inevitable, with a potential snowballing effect under some worse conditions. Delays have been rising since the beginning of deregulation. Delays in 1986 increased by 25 percent over 1985 at the nation's large hub airports, and increased another 13 percent in 1987. Although on-time performances have generally improved afterward, most of the improvements may be attributed to the carriers' practice of adding time to schedules rather than shaving actual transit time. More passengers are arriving 'on- time ' even though they are not arriving at their destination earlier (Dempsey and Goetz, 1992) . This may partially explain why the improvement in service, in terms of elapsed time, over 1978 is not that significant in the Syracuse-Portland market as indicated in Table 2.1. So although at the hub the higher frequencies of flights provide passengers traveling from the hub more choice and have the potential of reducing schedule delays, the over-concentration of traffic at the hub airport 40 will create inconvenience as well as missed or delayed flights for connecting passengers. Some of the passenger benefits could be offset by the increase in delays. The hub-and-spoke network has potential negative effects on passengers, causing them to consume more time in both aircraft and airports, a less pleasurable consumption of time. 2.3.4 Airline Hub Dominance and Monopoly Price It has been noticed in recent years that airfares are climbing fast, and nowhere is the situation worse than at the hubs for the nation's largest airlines. A USA TODAY analysis of 17.3 million tickets purchased at a dozen hubs from July 1996 to June 1997 found growing monopolies have led to ever- steeper prices, with hub fliers paying average fare increases of 9% - three times as large as at all airports. Airlines need monopoly opportunities to stem the intense competition, and the hub-and-spoke system gives them regional and city-pair market power. Most of the hub airports are dominated by a single airline. The big airlines have built up "fortress hubs" where they dominate the arrivals, departures and infrastructure (Rosato and Overberg, 1998) . Hub concentration translates into escalating fares, and the increase in fare is especially dramatic when a carrier established market dominance. Between Detroit and Boston, for example, where 41 Northwest controls 90% of the traffic, fares surged 127% during the twelve month period that ended June 30, 1997. Between Minneapolis and Milwaukee, 96% dominated by Northwest, fares jumped 57%. Between Atlanta aind Miami, 59% controlled by Delta, fares rose 39%. The dominance of large airlines at the hub airports has led to little price competition among the major airlines. Even when low-fare carriers enter a hub market, they usually control so little of the traffic that they can’t do much to bring fares down. 2.4 Remarks The development and expansion of hub-and-spoke structure in the airline industry are the result of the efforts of airlines to pursue network economies. Generally these types of economies are closely associated with traffic density on the routes between hubs and routes connected to hubs (spokes) and the economies emerged from the coordination of routes and flights over the entire network system. The development of the hub-and-spoke system has also become an essential marketing tool, enabling airlines to accommodate larger volumes of traffic from an increased number of city pairs, to avoid destructive competition, and to establish market power opportunities. Passengers tend to increase their welfare in air travel. 42 Travelers' welfare will vary with changes in air fares, in travel time, and in delays at airports. From this standpoint, the hub-and-spoke system has mixed implications for air travelers. While hub airports provide air travelers with a wider combination of service-fare options, their adverse effects are also well documented. Generally, two types of passengers are using hub airports. There are transit passengers who pass through when changing aircraft. These passengers, which form a large group of travelers at hubs, originate from other airports and are destined for other airports. Given the number of alternative air transport network available in the U.S., they normally have a choice of whether to take direct flight or to transit through one of several hubs. The full range of hub benefits is not passed on to them. Meanwhile, the fact that any one hub is dominated by a single airline does not constitute a monopoly position because these passengers can opt for alternative routings. Actually, hubs compete with each other for this type of traffic. Flight frequencies to and from the hub and the corresponding schedule delays could be important concerns for these travelers. The situation is a bit different for travelers residing at a hub airport city. They have no choice in terms of using the hub as an origin and return destination for their trips. 43 They are the main beneficiaries of hub privileges, especially business travelers who are generally less price sensitive but exhibit demands on service quality (e.g., time and frequency of flights, availability of lounge facilities and frequent flyer bonuses). However, the excessively high direct fare to and from hub airports may be a big burden for leisure travelers. So, the passenger implications of the hub-and-spoke system are quite unevenly distributed, both spatially and socially. 44 C H APTER 3 DIRECT OR STOPPED TRIPS; RELATIONSHIP BETWEEN ROUTING AND ORIGIN-DESTINATION DISTANCE One of the most important factors in air travel is city pair or origin-destination distance. Changing origin- destination distance will influence passengers' choice among various types of trips - direct or stopped. Thus, variations are expected to be found in the distribution of passengers choosing various routings among markets with different ranges of origin-destination distance. In this chapter, we will first briefly introduce the database used in this research, and perform statistical analysis to explore the association between various routings and city pair or origin-destination distance. The variations in routing structure for trips originating from different airports, and thus the impacts of origin airports will also be examined. 45 3.1 Air Passenger Database The database used in this research is compiled from U.S. Department of Transportation's Origin-Destination Survey of Airline Passenger Traffic which results from a survey of ten percent of all the passengers traveling on U.S. certified air carriers. The data collected encompass the full itinerary of the passenger, as it is reflected on the ticket, from initial origin to ultimate destination. The data resulting from the survey are compiled by the Data Administration Division of the Office of Aviation Information Management, Research & Special Programs Administration, of the U.S. Department of Transportation. The processed database containing thirty-one major passenger airports in the continental U.S.A. was obtained from Data Base Products, Inc., a Dallas Texas based company. The name and code of each airport is shown in Appendix A. There thirty-one airports are the origins and destinations of all the travels included in the database. This database provides with trip data for each airport as origin and destination respectively, including detailed 0-D routing information (destination airport, connecting airports), number of passengers, nonstop distance, itinerary distance and air fare for individual routing. This database was further reorganized and manipulated by the author to get aggregated trip information between each pair of the thirty-one airports. 46 1. Origin 2. Destination 3. Non-Stop Distance 4. Passengers Total Direct One Stop Two/More Stops 5. Itinerary Distance Total Direct One Stop Two/More Stops 6. Average Fare Total Direct One Stop Two/More Stops Table 3.1 Data Items of the Aggregate Passenger Database 47 Table 3.1 displays data items of this aggregated database. The thirty one airports are all EAA medium and large hubs offering various types of trips to destinations throughout the continental United States. According to the latest USDOT Ten Percent O&D Survey, the 1997 total (inbound and outbound) passenger traffic of these airports is 411,193,440 which accounts for 52.09 percent of that of 400 major passenger airports, and 51.96 percent of all airports. In terms of the revenue, the total passenger revenues of the thirty one airports reached $113,469,695,220, which accounted for about 56% of those of all airports. Meanwhile, these airports also had nearly 54 percent of the all domestic coupon passenger miles in 1997. The above figures reveal the importance of the thirty one airports in the U.S. domestic passenger air market, and suggests that the database provides a solid basis for further air travel analysis. It has been noticed that several airports are located very close to one another which leads to a very short O-D or city-pair distance. For instance, JFK Airport and LaGuardia Airport (LGA) are only 11 miles apart, and O'Hare Airport (CRD) and Midway Airport (MDW) are only 16 miles apart. Other airport pairs include Newark, New Jersey (EWR)-LGA (17 miles), E MR-JFK (21 miles), Washington National Airport (DCA)- Washington Dulles Airport (lAD) (24 miles), Houston Hobby 48 Airport (HOU)-Houston. Intercontinental Airport (lAH) (24 miles). Baltimore Washington Airport (BWI)-DCA (30 miles) and BWI-IAD (45 miles) . Since almost all the above airport pairs are located in the same city or metropolitan area, the travel between them could easily be achieved by ground transportation, and differs from other air trips, especially those long-haul trips. In order to avoid the bias which could be brought into the analysis by these short-distance trips, we exclude those trips in the same city (or metropolitan area) and trips with non-stop O-D distance shorter than 80 miles. As a result, there are 883 airport pairs included in the following analysis at an aggregate level. 3.2 Routing by Origin-Oestination Distance There are mauiy considerations when a passenger is making the decision regarding what kind of trip he wants to take - direct trip, one stop trip or two-and-more stop trip between a specific origin and destination pair. In addition to other factors, the non-stop or O-D distance between two airports can be of importance. It is not unusual that a person will never consider stopped trips if the distance between the origin and destination is short. However, with the increase in the O-D distance, it becomes more likely for that person to at least give stopped trips a thought when two cities are far apart. He 49 may be willing to sacrifice travel time or itinerary distance for some other possible benefits, such as a lower air fare. Also, with long O-D distance there is a possibility that a passenger may stop shortly somewhere on his way to the destination to meet somebody, for instance, at or near the airport. This could have the impact of raising the proportion of passengers flying stopped routes. In this section, we will perform analysis to explore the relationship between routing and O-D distance in air travel. On an aggregate basis, we are interested in identifying some possible patterns in the airline transportation. For example given an O-D market with a specific non-stop distance, what would be the possible distribution of passengers among direct, one-stop, or two/more-stop trips - aggregate market share of each route. Here, we want go beyond the individual personal choice and explore some possible tendency existing in the air travel at an aggregate level. Our goals here are: 1) to statistically test the independence/or interdependence between routing variable and O-D distance, and 2) to quantify the strength of the association between the two variables. Based on the non-stop or O-D distances for all the airport pairs, we can group trips among these airports into five general categories: 1) shorter than 500 miles, 2) 500 miles to 1,000 miles, 3) 1,000 miles to 1,500 miles, 50 Passengers Percent of Passengers Distance Direct 1 Stop 2/More Total Direct 1 Stop 2/More (l,OOOnuL) Stop Stop < 0.5 3,104,288 70,584 4,888 3,179,760 97.63 2.22 0.15 0.5 - 1.0 3,654,726 378,454 27,878 4,061,058 89.99 9.32 0.69 1.0 - 1.5 1,892,350 416,612 24,454 2,333,416 81.10 17.58 1.05 1.5 - 2.0 875,622 309,102 26,588 1,211,312 72.29 25.52 2.19 > 2.0 1,183,589 453,602 54,396 1,691,587 69.97 26.82 3.22 Total 10,710,575 1,628,354 138,204 12,477,133 85.84 13.05 1.11 Table 3.2 Routing by O-D Distance 4) 1,500 miles to 2,000 miles, and 5) more than 2,000 miles. In terms of the routing, there are three categories: 1) direct, 2) one stop, and 3) two and more stops. We try to explore if there are some systematic or regular changes in the proportion of each route type in response to the change in O-D distance. Tôüale 3.2 shows passenger traffic and its percentage for each routing type by O-D distance range, while the bar chart in Figure 3.1 visually portrays this relationship. Several 51 100.00 90.00 80.00 70.00 60.00 □ Direct □One Stop f 50.00 I >= Two Stop 40.00 30.00 20.00 10.00 0.00 'BSL. <0.5 0.5-1.0 1.0-1.5 1.5-2.0 > 2.0 Total Distance (1,000 mNe») FIGURE 3.1 ROUTING BY O-D DISTANCE (ALL TRIPS) interesting points can be observed from the table and chart. First, on an aggregate basis direct trip is the predominant routing type regardless of O-D distance ranges. Overall, the proportion of direct trips is more than 85%. This proportion remains above 70% in almost all the O-D distance groups, which indicates that direct service is attracting most of the air passengers. Second, with the increase of O-D distance, the proportion of direct trips gradually declines from around 98% in "shorter than 500 miles" category to about 70% in "more than 2,000 miles" category. Meanwhile, the percentage of one- stop trips is raised from 2% in "shorter than 500 miles" category to 27% in "more than 2,000 miles" category. Except for "1,500 miles to 2,000 miles" category, there is also a steady increase in the absolute number of passengers choosing one-stop trips. Overall, one-stop trips account for 13% of total trips. Third, there is a big jump in the absolute number of passengers flying two/more-stop trips when O-D distance is increased from "shorter than 500 miles" to "500 miles to 1,000 miles", and from "1,500 miles to 2,000 miles" to "more than 2,000 miles" respectively. Meanwhile its percentage is raised from 0.15% to 3.22%. In spite of this increase in the absolute term and percentage, two/more-stop trips account for only a very tiny fraction of all trips, which implies that when passengers are willing to choose stopped trips they 53 Average Itinerary Average Fare Distance (mile) ($) Hon Stop Direct One Two/ Direct One Two/ Distance Stop More Stop More (1, OOOini) Stop Stop < 0.5 310 602 1271 97.99 147.30 *294.30 0.5-1.0 751 994 1204 140.60 150.87 284.00 1.0-1.5 1201 1415 1713 171.26 164.23 322.53 1.0-2.0 1720 1937 2346 215.32 182.22 345.20 > 2.0 2423 2509 2838 263.72 215.11 358.57 Total 966 1686 2159 153.38 177.98 322.31 Table 3.3 Average Itinerary Distance and Air Fare prefer one-stop trips to trips with two or more than two stops. The above points can be further examined from the perspective of travel cost or air fares. Table 3.3 shows average itinerary distances, average air fares, and fares of unit itinerary distance for various trips. It can be seen that except for short-haul trips, passengers pay less when choosing one-stop trips. The average fares are lower for one-stop trips when O-D distance is longer than 1,000 miles. On the contrary. 54 passengers have to pay more them, non-stop fares to fly on two or more than two stop trips. This can partially explain why there is a noticeable rise in the proportion of one-stop trips when long distance travels are involved. The air fare structure and its variations will be further analyzed in detail in next chapter. 3.3 A Correspondence Analysis of the Association between Routing and O-D Distance Examination of Table 3.2 cind Figure 3.1 is a useful first step in studying the relationship between two variables of routing and O-D distance, following which we will perform crosstabulation analysis to statistically test this relationship and quantify the strength of the association between them. We can construct a 5 by 3 crosstabulation of routing and O-D distance. Two sets of statistical indices are used in this analysis. The first index is the Pearson Chi- Square statistic. It is calculated by summing over all combinations the squared residuals divided by the expected frequencies. In our analysis 55 where O^j and E^j are observed and expected frequency for cell or combination (i,j). The computed is then cougared with the critical points of the theoretical distribution to produce an estimate of how likely (or unlikely) this calculated value is if the two variables (routing and O-D distance) are in fact independent. The degrees of freedom in our case are (5-1) x (3-1) = 8. With respect to the calculated value, if the probability or the "observed significance level" of the test is small enough (usually less than 0.05 or 0.01), then we can reject the null hypothesis and conclude that routing variable and O-D distance are interdependent in distribution. While x^ index can indicate the independence or dependence of two variables or distributions, it can not deal with the strength and nature of the dependence of variables which is one of the central concerns in this analysis. A second set of statistical indices is used here to pursue the measure of association, of which the first is Cramer's V. Cramer's V is a chi-square-based measure which takes the following form: 56 where N is total number of cases and k is the smaller of the number of rows and columns. This statistics can attain the maximum of 1 for tables of any dimension. The second index of measure of association is Goodman and Kruskal's tau. It is computed by comparing the probabilities of error when predicting only from marginal totals and when predicting from the other (row or column) variable. It essentially reflects the reduction in error of prediction when incorporating information eibout one variable. Tau is defined as follows: , . (3.3) P(i) where P(l) and P(2) are the probabilities of error in predicting a variable when the information of the other variable is unknown or known respectively. If the observed significant level for tau is very small, we can generally reject the null hypothesis that tau is zero. Tau is calculated with both variables as dependent respectively. In this analysis, we are more interested in routing variable as the dependent. Using Cramer's V and Goodman and Kruskal's tau, the relative strength of the association between two variables could be thus explored in a quantitative manner. The result of crosstabulation analysis between routing 57 variable and O-D distance variable based on aggregate data is shown in Figure 3.2. Since the observed significance level for chi-square statistics is very small ( « 0.01), the hypothesis that routing variable and O-D distance variable are independent can be rejected. A relationship does exist between the choice of routing and non-stop distance between origin and destination. This can also be proved by other similar indices in Figure 3.2, such as likelihood ratio and Mantel-Haenszel test for linear association. From observed, expected and residual values of each cell we can see that the residual values are not very significant relative to the observed number of passengers for cells of direct trips compared with those of stopped trips, especially two/more-stop trips. This may imply that the share of direct trips is relatively stable in air travels, while stopped trips, especially two/more stop trips are more sensitive to distance between origin and destination. Examining measures of association, Craimer's V is around 0.2, and Goodman & Kruskal Tau is 0.07 with routing variable as dependent. When predicting routing variable with information about O-D distance can only reduce the error by about 7% in comparison with the prediction without knowledge of O-D distsince. This indicates that the existing relationship proved by chi-square statistics is not very strong. This is 58 DISTANCE DISTANCE RANGE by ROUTE ROUTING ROUTE Count Exp Val DIRECT ONE STOP TWO/MORE Residual STOP Rovr II 2 1 3 1 Total DISTANCE --- 3104288! 70584! 48881 3179760 < 0.5K 2729558 ! 414981.1! 35220.9! 25.5% 374730.0! -344397 1-30332.9! 3654726! 378454! 278781 4061058 0.5K - l.OK 3486079!529996.8 ! 44982.6! 32.5% 168647.4 ! -151543!-17104.6 ! 13923501 416612! 244541 2333416 i.OK - I.SrC 2003042(304527.3! 25846.3! 18.7% -1106921112084.7 I -1392.31 875622! 3091021 26588 ! 1211312 1.5K - 2.OK 10398101158084.8! 13417.21 9.7% -164188 ! 151017.2! 13170.8! 1183589! 453602! 54396! 1691587 > 2. OK 14520861220764.11 18737.01 13.6% -2684971232837.9! 35659.01 Column 10710575 1628354 138204 12477133 Total 85.8% 13.1% 1 .1 % 100.0% Chi-Square Value DF Significance Pearson 1015029.329 8 .00000 Likelihood Ratio 1071546.777 8 .00000 Mantel-Haenszel test for 950605.5701 1 .00000 linear association Minimum Expected Frequency - 13417.20 Approximate Statistic Value ASEl Val/ASEO Significance Phi .28522 .00000 *1 Cramer's V .20168 .00000 *1 Contingency Coefficient .27428 .00000 *1 Goodman & Kruskal Tau ; with DISTANCE dependent .01959 .00003 .00000 *2 with ROUTE dependent .07226 .00013 .00000 *2 *1 Pearson chi-square probability *2 Based on chi-square approximation Number of Missing Observations : 0 Figure 3.2 Crosstabulation; Routing by O-D Distance 59 not a very surprising result considering the dominance of direct trips in air travel. When stopped trips are more sensitive to O-D distance, one-stop trips, especially two/more-stop trips only account for a very small proportion in the market. Also, a low value of a particular measure does not say everything. It may just indicate that the variables are simply not related in the way to which the measure is sensitive, and more analysis and insights should be pursued. O-D distance does have an impact on passengers' routing choice while some other factors are playing their roles at the same time. 3.4 Origin Airports: Will They Make A Difference in Routing? In order to pursue the impacts of other factors on passengers' routing choice, we turn to thirty one major airports as origins of trips in this section. These 31 passenger airports are distributed throughout the continental United States, and have different activity levels in terms of departure and arrival of domestic passengers among them ranging from 184,380 at Dallas Love Field to 1,848,395 at Los Angeles Airport. Table 3.4 ranks these airports based on domestic departure and arrival of passengers, and the locations of these airports are depicted in Figure 3.3. Los Angeles Airport and Chicago O'Hare Airport outrank all the 60 Airports Departure Arrival Total Rank LAX 931,035 917,395 1,848,430 1 ORD 920,657 921.426 1,842,083 1 SFO 708,437 702,668 1,411,105 2 ATL 678,172 675.825 1,353,997 2 BOS 608.956 610.689 1,219,645 2 EWR 564,542 566,853 1,131,395 2 DFW 533,646 534.006 1.067.652 2 LGA 516.441 518.045 1,034.486 2 MIA 515,480 516,137 1.031.617 2 PHX 466,491 466,949 933,440 3 DEN 463,862 464.878 928,740 3 DTW 436,064 438.228 874,292 3 JFK 402,583 401.395 803,978 3 SEA 377,342 377.259 754,601 3 MSP 371,620 375,795 747,415 3 DCA 371,600 371.066 742.666 3 TPA 349,080 346.917 695,997 3 PHL 335,746 339,525 675,271 3 STL 321,119 324.016 645.135 3 lAH 322,841 322.071 644,912 3 BWI 285,040 287.602 572,642 4 MCI 268,566 268.947 537,513 4 lAD 262,937 263,821 526,758 4 MSY 251,845 251,046 502,891 4 MDW 244,512 246,058 490,570 4 CLE 241,478 242.210 483,688 4 PIT 185,735 185,965 371,700 4 HOU 180,968 180.727 361,695 4 CVG 161,316 159.824 321,140 4 MEM 110,883 111.757 222,640 4 DAL 92.243 92.137 184,380 4 Table 3.4 Airports Ranked by Departure and Arrival of Passengers 61 MS? BOS DTW PIT PHI JFK/LGA sro CVG MCI STL LAX MEM s PHX DFW/DAL ATL lAHTlOU TPA MU FIGURE 3.3 THIRTY ONE MAJOR PASSENGER AIRPORTS other airports in total passenger traffic. In order to examine airports' impacts on routing, we summarize trips with each airport as an origin into the same categories of routing and O-D distance as those for the total trips. Crosstabulation analysis is performed and measures of association are obtained for trips with each airport as origin. Generally, the observed significance levels of chi- square statistics for all airports are very small, far below 0.01. So, the relationship between routing variable and O-D distance still prevails for trips from each airport. However, when we examine measures of association specifically Cramer's V and Goodman/Kruskal Tau (with Routing being dependent), we do find some differences among the airports. Cramer's V rsinges from 0.0728 to 0.4174 while Goodman/Kruskal Tau ranges from 0.0082 to 0.3074. Based on the overall distribution of the two measures of association, as well as the similarity among airports with respect to these measures and passenger route structure, we group the airports into three classes shown in T a b l e 3.5. The first class contains airports which have Cramer's V larger than 0.3 and Goodman/Kruskal Tau bigger than 0.20. At extreme, LGA has these two values of 0.4174 and 0.3046 respectively. Trips from airports in this class have a relatively strong association between routing and O-D 63 distance. Almost all these airports are sending trips involving a sharp decline in the proportion of direct trips and a dramatic rise in the percentage of stopped trips, especially one-stop trips with the increase in O-D distance. For instance, from DCA the percentage of direct trips is below 20% and the proportion of one-stop trips is above 70% when O-D distance is more than 2,000 miles (Figure 3.4). A close look at these airports indicates that except for LGA, all the airports in this class are not very large from the point of view of passenger flows with levels at 3 or 4. They are primarily local and regional airports. It is more likely for passengers departing from these airports to stop at a larger connecting (hub) airport (s) in order to arrive in their destinations. Trips from airports in the second class also demonstrate the pattern of rise and decline in the proportions of direct and stopped trips. However, the changes are not as striking as those in the first class. Generally, the percentage of direct trips stands above 60% and the proportion of one-stop trips barely goes beyond 30%. Figure 3.5 depicts trips from Boston. In the third class of airports, we find that direct trips significantly dominate in almost all the O-D distances. Generally the proportion of direct trips is well above 70% and the percentage of one-stop trips is below 20%. Many large and 64 Airport Cramer's V Goodman/Kruskal Tau (with Group ROOTING dependent) LGA 0.4174 0.3046 1 DCA 0.4091 0.3074 1 HOU 0.3895 0.2974 1 CLE 0.3562 0.2399 1 MSY 0.3429 0.2246 1 BWI 0.3355 0.2091 1 SEA 0.3252 0.1959 1 PHX 0.3046 0.1769 1 TPA 0.2864 0.1551 2 MCI 0.2766 0.1474 2 BOS 0.2763 0.1386 2 PHL 0.2628 0.1248 2 MEM 0.2446 0.1082 2 PIT 0.2325 0.0986 2 MIA 0.2108 0.0753 3 ATL 0.2085 0.0774 3 MDW 0.2075 0.0838 3 DTW 0.1970 0.0702 3 DEN 0.1963 0.0728 3 SFO 0.1910 0.0623 3 STL 0.1897 0.0686 3 O/G 0.1758 0.0513 3 LAX 0.1734 0.0511 3 lAD 0.1656 0.0445 3 EÎVR 0.1655 0.0463 3 lAH 0.1567 0.0448 3 MSP 0.1397 0.0358 3 ORD 0.1209 0.0231 3 DFW 0.0835 0.0112 3 JFK 0.0726 0.0082 3 Table 3.5 Airports Grouped from Crosstabulation Analysis 65 aoiract ^ 50.00 □One Stop l>= Two Stop 0.00 <0.5 0.5-1.0 1.0-1.5 1.5-2.0 > 2.0 Total Distance (1,000 miles) FIGURE 3.4 ROUTING BY O-D DISTANCE (ORIGIN=WASHINGTON NATIONAL AIRPORT) 100.00 90.00 80.00 70.00 60.00 □ DIncI □One Stop ÿ 50.00 ■>= Two Stop 40.00 30.00 20.00 — 10.00 0.00 <0,5 0.5-1.0 1,0-1.5 1.5-2.0 > 2,0 ToM Distance (1,000 miles) FIGURE 3.5 ROUTING BY O-D DISTANCE (ORIGIN=BOSTON) 100.00 90.00 80.00------ 70.00 il? 80.00 □Olract aK 50 .00 DOneStop ■>= Two Stop 40.00 Os 00 30.00 20.00 ' — 10.00 0.00 «0.5 0.5.1.0 10.1,5 1.5.2.0 Total Distance (1,000 miles) FIGURE 3.6 ROUTING BY O-D DISTANCE (0RIG1N=0’HARE AIRPORT) 100.00 90.00 80.00 70.00 60.00 □ Direct □One Stop □>g Two Stop 40.00 s 30.00 2 0 .0 0 -- 10.00 0.00 <0.5 1.0.1.5 1.5-2.0 Total Distance (1,000 miles) FIGURE 3.7 ROUTING BY O-D DISTANCE (ORIGIN=DALLAS/FORT WORTH) 100,00 90,00 90.00 70.00 60.00 BOIract □One stop □>= Two Stop 40.00 30.00 20.00 10.00 0.00 <0.5 0.5.1.0 1.0.1.5 1.5.2.0 »2.0 Total Distance (1,000 miles) FIGURE 3.8 ROUTING BY O-D DISTANCE (ORIGIN=LOS ANGELES) leading passenger airports can be found in this class, such as ORD, DFW, and LAX (Figures 3.6, 3.7, 3.8), etc.. These large airports are important connecting airports and performing hub functions where passengers transfer to another flight then to reach their destination directly. Direct trips will be predominant from these airports. 3.5 Ranarks While msiny potential factors influence various routings of air travel, nonstop distance between origin and destination does have an impact on the distribution of passengers among varied trips. Generally, with the increase of O-D distance the proportion of direct trips declines while the percentage of stopped trips, especially one-stop trips rises gradually. Because direct trips still dominate passenger air travels, the above relationship between routing and O-D distance is not very strong statistically. Although overall the proportion of one-stop trips is less than 20%, in some markets, it could be as higher as 70%. In long-haul travels, passengers are more likely to fly one-stop trips, which means longer itinerary distance and travel time, in exchange for savings in air fare. It is also likely that in some markets one-stop routes are the only option for passengers traveling long distance range. Thus the availability of scheduled flights and possible price 71 incentive may both contribute to the increase of passengers flying one-stop routes in the long-haul markets. Compared with one-stop trips, two/more-stop trips account for only a tiny fraction in the market, and their fares are also more expensive than those of direct and one-stop trips. Trips from different airports show some variations in their routing structure. Small and local airports are more likely to send passengers to major hub or connecting airports where they can transfer to other flights to their destinations. On the contrary, passengers originating from large hub airports are generally travelling in a direct way. This is one of the advantages or benefits provided by major hub airports to passengers living in the hub city. 72 CHAPTER 4 DOMESTIC AIRLINE PRICING ANALYSIS Airline fare is an important component in a commercial airline's profit. It will affect capacity and other dimensions of service quality. For airline passengers, the levels of air fares also seirve as an important decision making variable for them to decide which airline to fly, which route to fly and also when to fly. It has been indicated that fares have been in a continuing state of change since deregulation. While the overall average fare paid by all airline passengers increased, on some routes, fares have been reduced by deregulation to levels below those of pre-deregulation age. But on other routes, post-deregulation fares have doubled or more. In the following discussions of airline pricing, the general trends of airline aggregate pricing data and variations within these data will be examined. 4.1 Air Fare Variations Before airline deregulation, air fares were regulated by 73 the CAB which attempted to take all costs variables into account, disallowing those that appeared to reflect operational inefficiency. The CAB then averaged the allowable costs into one overall formula. This represented a consolidated industry cost level, intended to reflect efficient operation under existing technology. The CAB formula recognized that costs vary inversely with segment length and, therefore, the formula included a taper, so that fare per mile sloped downward with increasing distance. Figure 4.1 was obtained as a reference from the analysis of deregulation by Brenner, Leet and Schott (1985). It indicates that in 1978, fares for the city pairs ranged from a high of 29 cents per mile for a 119-mile trip, down to 9 cents per mile for transcontinental trips. Similar graphical relationships are constructed using the aggregate airline passenger data involving 31 major airports. In order to obtain the general trend and avoid the bias imposed by those routes with only short city-pair distances and small passenger volumes, the analysis is focused, on the one hand, on trips with city-pair distance longer than 80 miles. On the other hand, for the total trip data, those routes with passenger volume smaller than 1,000 are also dropped. 74 cents 3 0 A pril 1978 20 . 10 ^ w --- • 500 1,000 1,500 2,000 2,500 (miles) Figure 4.1 Fare Per Mile vs. City-Pair Distance, 1978 Figure 4.2 and Figure 4.3 display the relationship between air fare and city-pair distance, and fare per mile and city-pair distance respectively. Air fares of selected city/airport pairs with various distances are also presented in Taible 4.1. It can be seen that while there was a consistent pattern of fare per mile when air fares were regulated, the pattern is less clear and involves a great variation in current post-deregulation time. The absolute air fares and fares per mile vary widely in Figure 4.2 and Figure 4.3, even for markets of similar stage length. The absolute fare difference could be as big as $100, and fare per mile could be as different as 80 cents or even a dollar. For instance, from 75 700 600 500 400 m 300 o\ 200 A ■ 100 a . 0 500 1000 1500 2000 2500 3000 OiaUnc«(MHt) FIGURE 4.2 AVERAGE FARE VS. CITY-PAIR (O-D) DISTANCE (ALL TRIPS) 1.40 1.20 1.00 ][0.80 0.40 0.20 « ■■ 0.00 0 500 1000 1500 2000 2500 3000 FIGURE 4.3 FARE PER MILE VS. CITY-PAIR (O-D) DISTANCE (ALL TRIPS) Clevelcind (CLE) to Detroit (DTW), the average air fare is $116.45, while the average fare is only $53.99 from LaGuardia Airport (LGA) New York to Philadelphia (PHL) . The corresponding fares per mile are $1.23 and 56 cents respectively, even though these two markets are within 2 miles of the same city-pair distance. Figure 4.1 shows that the difference in fare per mile from short trips to intercontinental trips are generally within 20 cents in 1978 prior deregulation. Figure 4.3 shows, however, the difference could be as much as a dollar. Even though those high fare routes of short-run were dropped, the difference in fare per mile could still reach the level of nearly 70 cents. It can also be noticed that on the post-deregulation fare curve, average fare per mile tapers very rapidly in the short distance range, say, within 500-mile city-pair distance. The primary differences in fare per mile among airline markets are also found within this short distance range. This contrasts significantly to Figure 4.1 where fare per mile curve tapers moderately within the same extent of distance. It is clear from this observation that in the current deregulated markets, it's common for passengers to pay more in absolute dollars for a short trip than for a long one. For example, the average $186 Los Angeles(LAX)-St. Louis(STL) fare is $26.5 less than the Cincinnati(CVG)-LaGuardia(LGA) fare, and around $53 less 78 City/Airport Pair Distance Average Fare Average Fare Per (mile) ($) Mile ($) CLE-DTW 95 116.45 1.23 LGA-PHL 96 53.99 0.56 BWI-EWR 119 67.51 0.57 CVG-MDW 249 117.07 0.41 EWR-PIT 319 130.83 0.41 DFW-MEM 432 165.47 0.38 CVG-LGA 585 212.52 0.36 CLE-MEM 622 238.97 0.38 LGA-ORD 733 196.75 0.27 EWR-PIT 872 226.64 0.26 MSP-PHL 980 254.87 0.26 BOS-STL 1046 242.44 0.23 HOÜ-PIT 1131 238.87 0.21 BWX-DFW 1217 219.96 0.18 HOU-PHL 1335 229.44 0.17 DEN-IAD 1452 227.94 0.16 LAX-STL 1593 186 0.12 DFW-SEA 1660 217.43 0.13 SFO-STL 1736 187.12 0.11 lAH-SBA 1874 200.93 0.11 DCA-PHX 1979 221.14 0.11 CLE-LAX 2053 211.85 0.10 JFK-PHX 2153 191.11 0.09 PIT-SFO 2253 241.57 0-11 BWI-SEA 2335 189.79 0.08 DCA-SFO 2442 212.47 0.09 LGA-SFO 2579 284.66 0.11 BOS-LAX 2611 1 259.41 0.10 Table 4.1 Average Air Fares of Selected City/Airport Pairs 79 than the Clevelauad (CLE)-Memphis (MEM) fare, even though the former trip is 1,593 miles, or about 2.5 times as long as the mileage of the latter two trips, which are 585 miles and 622 miles respectively. The pricing disparities indicated in Figures 4.2, 4.3 and Table 4.1 represent a general trend in passenger air travel in the post-deregulation era. Fare disparity has become an on going phenomenon. 4 .2 Air Fares v s . Various Routings In Section 4.1, the general trends in the post deregulation air fares are studied using aggregate data of total trips among 31 airports. In this section, air fares will be further investigated for various types of trips - direct or non-stop trips, one stop trips, and two-and-more stop trips. The purpose of this investigation is to explore the impacts of routing variable on air fares, as well as the general patterns of air fare change with distance for each type of trips. In the database, passenger volumes display a great disparity among various routes from one type of the trip to the other. For example, the largest passenger volume for direct trips is 101,834 (LAX-SFO), while the largest passenger volumes for one stop trips and two-and-more stop trips are 11,055 (BOS-SFO), and 1,822 (ORD—SFO) respectively. Meanwhile 80 there are also great internal variations in traffic volume for each trip type. Considering this situation, varied minimum annual passenger volumes are used as thresholds to include city/airport pairs or routes in the analysis - 10,000 for direct trips, 1,000 for one stop trips, and 500 for two-and- more stop trips. Again, those routes shorter than 80 miles are also dropped. The relationships between average air fare and O—D distance for various types of trips are shown in Figures 4.4, 4.5, and 4.6. The corresponding curves of fare per mile are displayed in Figures 4.7, 4.8 and 4.9. As discussed in the previous section, the pattern of fare change with 0-D distance portrayed by scattered points is less clear, especially for the average air fare. In order to explore the general tendency, we try to fit trendlines to the scattered data. Since the distribution pattern is not very obvious, except for the fact that average fares generally rise with increasing 0-D distance, several types of functions have been tried including linear, logarithmic, power and exponential. As expected, the R square values for all these functions are not very high. Comparatively linear trendline functions tend to be better representing the distribution of average fare data than the rest for all the trip types. So, linear trendlines are adopted to reflect the general tendency in average fare change with O- 81 700 800 Trendline (R*=,4043) Lower and Upper 90% Cl 500 400 I 300 s 200 100 5» y ---- 0 500 1000 1500 2000 2500 3000 Oletence(MNe) FIGURE 4.4 AVERAGE FARE VS. CITY-FAIR (O-D) DISTANCE (DIRECT TRIPS) 700 600 Trendline (R*=.1067) Lower and Upper 90% Cl 500 400 300 s 200 100 500 1000 2000 2500 3000 FIGURE 4.5 AVERAGE FARE VS. CITV-PAIR (0-D) DISTANCE (ONE STOP TRIPS) TOO Trendline (R*=.1224) Lower and Upper 90% Cl 600 500 400 ■ f u.! 300 V t #' 200 100 500 1000 1500 2000 2500 3000 DIaUnce (MHe) FIGURE 4.6 AVERAGE FARE VS. CITY PAIR (O-D) DISTANCE (TWO-AND-MORE STOP TRIPS) 1.40 1.20 - .TrendNn»(R'=.6701) ■Lower and Upper 90% Cl 1.00 0.80 g 0.80 00 LA ■■ 0.40 0.20 0.00 500 1000 2000 2500 3000 FIGURE 4.7 FARE PER MILE VS. CITY PAIR (O-D) DISTANCE (DIRECT TRIPS) 1.40 1.20 TrendHne(R*=.7201) Lower and Upper 90% Cl 1.00 0.80 s > • ■ 0.40 f a 0.20 ■ ■■ 0.00 500 1000 2000 2500 3000 FIGURE 4.8 FARE PER MILE VS. CITY PAIR (O-D) DISTANCE (ONE STOP TRIPS) 1.40 1.20 Trendline (R^=.7414) Lower and Upper 90% Cl 1.00 0.80 g 0.60 0.40 ■ ■ • • # 0.20 0.00 500 1000 1500 2000 2500 3000 Dlatance(Mile) FIGURE 4.9 FARE PER MILE VS. CITV-PAIR (0-D) DISTANCE (TWO-AND-MORE STOP TRIPS) D distance. These linear trendlines are as the following; Direct Trips: FR « 0.0607 D + 100.21 (4.1) (F = 276.2462, Sig. F = .0000) One Stop Trips: FR = 0.0261 D + 142.99 (4.2) (F = 47.0201, Sig. F = .0000) Two and More Stop Trips FR = 0.0434 D + 258.40 (4.3) (F = 35.3001, Sig. F = .0000) where, FR is the average fare in dollars and D is the 0-D distance in miles. Similar efforts have been made to fit trendlines to fare per mile data. Compared with average fare, the data distribution of fare per mile is much more regular. So, the R square values for power trendlines adopted are generally around 0.70. These trendlines of power functions are described as follows, where FPM is fare per mile in dollars. Direct Trips: FPM = 11.999 D"®"" (4.4) (F = 826.5086, Sig. F = .0000) One Stop Trips: FPM = 67.716 D"®-"®» (4.5) (F = 1013.6895, Sig. F = .0000) Two and More Stop Trips: FPM = 89.184 d ~®-*“* (4.6) (F = 725.4401, Sig. F = .0000) Because of the wide variations in the data distribution for averages fare and fares per miles, upper and lower 90 88 percent confidence intervals (CI) are also included in each of the above graph along with the estimated trendline. Generally, the trend in air fare change discussed in the previous section are held in all types of trips, which is that average air fares increase and fares per mile decline with increasing 0-D distance. However, because of the difference in trendline slopes and intercepts for various trip types, several interesting relationships can be derived from the generalized trendline functions. First, for airline passengers, two-and-more stop trips seem to be the least economical choice, since in all the 0-D distance ranges, passengers have to pay much higher absolute fares. This is also true from the point of view of fare per mile, especially for those short-haul trips. Generally speaking there is a $100 gap in air fare between two-and-more stop trips and one stop trips, as well as direct/nonstop trips. From just the general trendlines, it seems that the fare gap between two-and-more stop trips and one stop trips is becoming bigger, while the trendline slopes are 0.0434 for the former, and 0.0261 for the latter. This is an interesting finding. We, however, should be a bit cautious about it since the real data distributions of one stop trips and two-and-more stop trips involve great variations, and the general trendlines may not be able to portray the entire picture precisely. Considering the 89 relatively wide 90 percent confidence intervals for these two types of trips, the relationships between the fares at different distance ranges could be varied and complicated. It has been a widely-held public impression that direct or nonstop trips are the most convenient, and meanwhile the most economical ones from the viewpoint of air fares, since they involve short travel distances. However, that impression is only partially valid. Our data show that the superiority of direct trips to stopped trips is actually distance dependent, which means that when the origin-destination distance is over certain level, direct/nonstop trips will lose their economic advantages (i.e., air fares), one stop trips instead will become the most economical choice. This situation can be revealed in Figure 4.10 by the intersection of direct trip trendline and one stop trip trendline. It can also be noticed that this intersection occurs at the distance roughly between 1,000 miles and 1,500 miles. Beyond this distance of intersection, air fares of direct, nonstop flights exceed those of one stop flights at a rapid rate, and the gap between these two types of fares become increasingly larger. Table 4.2 is constructed intending to verify this point by organizing average air fares into five 0-D distance categories. It's clear that beginning with the category of 1,000 - 1,500 miles, the $164.23 average air fare for one stop trips becomes lower 90 than that of direct trips which is $171.26. In the next two distance categories, the differences between these two types of fares are $33 and $49 respectively. Overall, passengers pay about $25 more traveling one stop trips than direct trips ($177.98 vs. $153.38). But we have to recognize the dramatic difference between short-run auad long-run flights. It's more economical for passengers to fly direct trips over the routes with relatively short origin-destination distances, and choose one stop trips for city pairs involving long distances. City Pair Average Trip Fare ($) Distance (1,000 mile) Direct One >= Two Total stop Stop < 0.5 97.99 147.30 294.30 99.39 0.5 - 1.0 140.60 150.87 284.00 142.54 1.0 - 1.5 171.26 164.23 322.53 171.59 1.5 - 2.0 215.23 182.22 345.20 209.66 > 2.0 263.72 215.11 358.57 253.73 All 153.38 177.98 332.31 158.57 Table 4.2 Air Fares vs. City/Airport Pair (0-D) Distance 91 460 400 360 300 DIracI _ 250 1 Stop >°2Stop 200 ISO 100 500 1000 1500 2000 2500 3000 3600 O iitm n (MW#) FIGURE 4.10 AVERAGE FARE VS. CITY-FAIR (O-D) DISTANCE (GENERAL TREND) 3.5 2,5 DIract 1 Stop >"2 Stop 1.5 0.5 5001000 1500 2000 2500 3000 3500 FIGURE 4.11 FARE PER MILE VS. CITY PAIR (O-D) DISTANCE (GENERAL TREND) In. Figure 4.11, three curves are displayed representing generalized patterns of three types of trips in the change of fare per mile with market distance. Comparing the parameters of trendline functions, it's apparent that the difference in fare per mile among various types of trips is tremendously large in the markets of short-run, especially where the origin-destination distance is shorter than 500 miles. According to the generalized patterns, on the routes with extremely short O-D distance fares per mile of one stop trips and two-and-more stop trips are nearly 7 times and 9 times as high as that of direct trips. These are the markets in which direct trips have absolute advantage over stopped trips. However, with increasing 0-D distance, this gap in fare per mile narrows rapidly. Although for long-run trips, fare per mile of one stop flights is a couple of cents lower than that of direct flights and a couple of cents higher than that of two-and-more stop trips, fares per mile generally converge toward a stable level around 10 cents. This relationship can be partially explained by the difference between city pair or 0-D distance and the actual itinerary distance in various markets. City pair distance represents the spatial separation of two cities or airports, and it is generally the shortest air travel distance. However, because of various routings or flight types, the actual 94 mileage or itinerary distance passengers fly could be much longer than the city pair distance. Itinerary distance generally increases with increasing number of stops of a flight. Table 4.3 shows itinerary distances of various trip types in the markets with different city pair distances. The table indicates that in the market of short-run, the disparity in itinerary distance between direct trips and stopped trips is great, with average itinerary distances of one stop trips and two-and-more stop trips are two times and four times as City Pair Average Itinerary Distance Itinerary Distance Distance (miles) Ratio (1,000 miles) Direct One Two and Total One Two and Stop More Stop Stop: More Direct Stop : Direct < 0.5 310 602 1271 318 1.94 4.10 0.5 - 1.0 751 994 1204 777 1.32 1. 60 1.0 ■- 1.5 1201 1415 1713 1244 1.18 1.43 1.5 - 2.0 1720 1937 2346 1789 1.13 1.36 > 2.0 2423 2509 2838 2459 1.04 1.17 All 966 1686 2159 1074 1.75 2.23 Table 4.3 Itinerary Distance vs. City Pair (0-D) Distance 95 long as that of direct trips. In the market of long-run, this disparity tends to become smaller, with average itinerary distances of one stop trips and two-and-more stop trips being only 4 percent and 17 percent longer. This situation suggests that on different routes each 0-D mileage actually involves various itinerary distances. Since air fares are still somehow mileage-related, on the routes of short-run where the actual itinerary distances of various trip types could be tremendously different, the variation in average fares per mile is expected to be higher. Taking this consideration into account, we can also expect that the average fare per itinerary mile would be lower than fare per 0-D mile. Generalized patterns of fare per itinerary mile of three trip types are presented in Figure 4.12. Conçjared with Figure 4.11, it is not difficult to find that the curves of fare per itinerary mile have more gentle slopes, and the top values of one stop and two-and-more stop trips are much lower. Table 4.4 organizes average air fares and fares per mile into five itinerary distance categories. The distinctions between data in this table and those in Table 4.2 can be easily identified. The disparity between itinerary distance and city pair origin-destination distance, as well as a mileage-related fare structure can only partially explain the variations in air fare change. It is inconceivable that there would be 50 96 0.8 ijO.6 i >= 2 Stop 0,4 0.2 0 500 1000 1500 2000 2500 3000 3600 4600 5000 Olrtanct(MHt) FIGURE 4.12 FARE PER MILE VS. ITINERARY DISTANCE (GENERAL TREND) 500 450 400 360 300 S “• 250 VO 00 200 ISO 100 0 500 1000 1500 2000 2500 3000 3600 4000 4500 5000 DltUnce (MHe) FIGURE 4.13 AVERAGE FARE VS. ITINERARY DISTANCE (GENERAL TREND) Average Fare ($) Fare Per Mile ($) Itinerary Distance (1,000 Direct One Two Total Direct One Two Total miles) Stop and Stop and More More Stop Stop < 0.5 97.90 143.86 252.93 96.70 0.32 0.34 0.53 0.31 0.5 - 1.0 140.60 148.01 245.30 143.30 0.19 0.19 0.29 0.19 1.0 - 1.5 171.26 157.48 292.05 167.81 0.14 0.13 0.23 0.14 1.5 - 2.0 215.23 172.34 324.47 206.57 0.13 0.10 0.19 0.12 > 2.0 263.72 210.11 355.76 249.84 0.11 0.09 0.13 0.10 Table 4.4 Air Fare vs. Itinerary Distance percent, even 100 percent difference in fares on routes of similar distances if distance is the only explanatory variable. This indicates that the marketplace pragmatics of varying competitive pressures on different routes, characteristics of airports, as well as airlines' specific network organizations, can be more important. In next section, air fares of trips originating from two major passenger airports in Chicago - Midway Airport and O'Hare Airport will be compared and examined. 99 4 .3 Air Fares amd Origin Airports Like the fare variations found among various types of trips, air fare differences can also be identified among airports which have different status in the national market of air transportation. In this section, we'll analyze the air fare variations among the nation's leading 400 airports, as well as among the thirty one airports in oar database. Also, we'll take a close look at the fare disparity between O 'Hare Airport and Midway Airport in Chicago. Airports Annual Annual Average Average Coupons Passengers Revenue Fare ($) Fare Per (million) ($million) Itinerary Mile ( Cents) Large Hub 439 66,302.29 151.03 13. 66 1.3 M e d i u m Hub 228 28,519.79 125.07 13.09 1.4 Small Hub 81 12,098.01 150.09 16.24 1.6 Non—Hub 42 6,549.60 156.80 17.55 1.8 Total 790 113,469.69 143.74 13.92 1.4 Table 4.5 Air Travel Data at Nation's Leadiag Airports (1997) 100 According to EAA's hub classification scheme, four categories can be identified among the nation's 400 leading passenger airports: (1) large hub ports, (2) medium hub ports, (3) small hub ports, and (4) non-hub ports. Passenger traffic, revenues, air fares and average number of coupons are summarized in Table 4.5. It can be seen that large hub and medium hub airports dominate the nation's air transportation market, in terms of the annual passenger traffic and revenues. Comparatively, small hub airports, especially non-hub airports account only a small fraction in the market. Also, it can be found that on average a passenger holds about one coupon from large hub airports, while the average number of coupons each passenger at small and non-hub airports is nearly two. This echoes our findings in the previous chapter that more passengers traveling on direct flights from origin airports which are larger hubs. Regarding the air fares, passengers traveling from non hub airports are paying the highest prices compared with those from hub airports. Among hub airports, large hubs and small hubs are charging their passengers similar higher fares while the average passenger fare at medium hubs is about $25 lower. The comparable situation can also be found for average fare per itinerary mile. Travel costs from medium hub airports are generally lower than the national average. It seems that 101 medium hubs provide a better balance between the convenience of travel that passenger can enjoy at hubs and the cost to obtain the service. Large hub airports are more likely to have the airline dominance and price monopoly, while non-hub small airports are more likely to be under-served with few choices. Figure 4.14 and Figure 4.15 depict the average air fare and average fare per itinerary mile at each of the thirty one airports in the database. Since these airports are large and medium hubs, their average fare levels are similar to those of large hubs as indicated in Table 4.5. It can be noticed that, on the one hand, those airports with large passenger traffic flows, such as SFO, LAX, BOS, DFW, EWR, have average fares above the group average. On the other hand, most of those airports with above group average fares are located in the peripheral areas of the contiguous United States, which means that the average itinerary distances from these ports tend to be longer with their destinations all over the country. As a result of this. Figure 4.15 shows that such airports as SFO, SEA, lAX, JFK, EWR, lAD have their fares per itinerary mile below the group average. So, the status of an airport and its geographical location can both contribute to the fare variations among nation's airports. Next, we will take O'Hare Airport and Midway Airport as examples to examine the fare disparity among airports with quite different status. 102 250.00 200.00 M«en = 151.33 150.00 r r 1" 100.00 S 50.00 0.00 A Mil M M M g I M g M Mrpofts FIGURE 4.14 AVERAGE FARE AT AIRPORTS 0.2500 0.2000 Mean = 0.16 S 0.1500 £ 0.1000 O.OGOO 0.0000 Airpoita FIGURE 4.15 AVERAGE FARE PER ITINERARY MILE AT AIRPORTS O'Haré Airport and Midway Airport are two primary passenger ports in Chicago metropolitan area, separated by 16 miles. It has been claimed that O'Hare Airport handles more passengers and aircraft operations than any other airport in the world, ^proximately 180,000 travelers pass through O'Hare each day. In 1997, O'Hare served over 70 million passengers. In our data set containing 31 passenger airports and only domestic passenger flows among them, the total passenger volume (both arrival and departure) at O'Hare is 1,842,083, just next to Los Angeles in rank. O'Hare Airport is the major hub of United Airlines. Nine other domestic passenger airlines also offer frequent service at O'Hare, including such large airlines as Northwest, Continental, TWA, Delta and American. Losing its primary status to O'Hare in the 1960s, Midway Airport currently offers travelers over 200 daily flights on 15 airlines serving over 70 destination cities. Southwest Airline is taking the leadership role among others at Midway, offering a variety of routes to the cost conscious traveler. Midway served over 9.6 million passengers on 255,700 flights in 1996. In our database, Midway's total arrival and departure passenger volume is 490,570, ranking 25th among the thirty one airports. Both O'Hare and Midway have close proximity to Chicago downtown, and are in superior locations offering convenience 105 to travelers. Since they are 16 miles apart, both airports will have effectively the same origin-destination distances to various destinations. Itinerary distances for most of the routes are also very similar. Because of this unique situation, it will be interesting to take a look at air fares for various routes from the two airports. From the point of view of mileage-related air fare structure, air fares from O'Hare and Midway ought to be similar. However, our data show that there is an enormous disparity in air fare for trips from the two airports. Table 4.6 summarizes the average air fares for trips to 29 destination airports/cities originating from Midway and O'Hare. On average, air fares of trips from O'Hare are 43% higher than those from Midway, taking into account all types of flights. Some differences can be noticed among routes. For example, the average O'Hare-JFK fare is about 4% higher than the $103.72 fare from Midway to JFK, which is the lowest discrepancy. The average O'Hare-Minneapolis (MSP) fare is nearly 150% higher than the Midway-Minneapolis fare. Some high disparities can be found related to such destinations as San Francisco (SFO) , Washington Dulles (lAD), Washington National (DCA) , Cincinnati (CVG), LaGuardia (LGA) , Seattle (SEA) , etc. . The situation in fare discrepancy in direct trips and one stop trips is generally similar to that of total trips. Two-and- 106 Destinatio n. Total Trips Direct Trips One Stop Trips Two an d More Airport Stop Trips MDWORD MDW ORD MDN ORD MDW ORD ATL 81.06 104.29 79.35 100.18 113.64 183.60 359.13 293.24 BOS 148.77 204.61 152.21 204.66 146.49 192.58 279.50 320.92 BWI 66.44 95.88 66.44 94.84 66.39 99.79 304.76 CLB 54.35 70.07 54.27 67.97 108.07 176.16 359.71 CVG 104.87 170.82 104.31 169.33 183.20 260.16 217.21 DAL 39.72 20.76 39.86 DCA 92.19 159.32 106.97 159.53 85.24 140.34 367.50 351.98 DEN 117.97 144.19 116.97 139.57 131.23 206.66 128.31 379.03 DFW 134.34 199.79 146.25 197.53 136.67 212.52 112.73 324.93 DTW 58.63 77.92 58.53 76.28 124.89 202.84 237.12 EWR 108.39 141.63 107.36 139.64 117.74 192.57 179.99 230.17 HOU 99.71 133.04 99.62 164.74 102.82 127.16 196.00 325.23 lAD 78.44 141.84 71.84 139.97 133.11 142.68 273.48 lAH 117.01 138.69 113.45 135.28 132.24 218.81 177.16 334.08 JFK 103.72 107.78 108.70 99.88 99.28 158.09 244.42 LAX 136.23 212.31 139.46 207.67 123.48 212.92 132.80 373.22 LGA 121.57 196.73 106.87 195.44 126.56 212.62 768.00 321.88 MCI 59.76 80.69 59.52 78.42 71.10 142.13 336.36 347.07 MEM 134.93 174.35 113.64 173.33 135.33 171.17 115.14 297.96 MIA 122.88 153.47 120.52 149.81 131.70 172.03 120.19 271.32 MSP 75.24 185.15 75.03 183.76 387.34 309.86 342.82 326.58 MSY 97.48 120.06 97.20 120.12 97.73 113.84 159.71 326.40 PHL 138.71 199.89 159.79 198.52 134.80 215.96 382.21 292.65 PHX 110.72 140.89 111.29 140.95 106.48 124.45 194.90 290.97 PIT 167.91 187.78 158.27 184.46 168.81 260.23 414.37 SEA 141.11 223.40 136.36 223.18 148.45 208.39 434.59 328.46 SFO 138.61 250.70 140.25 248.06 131.92 239.31 140.92 353.86 STL 55.74 76.89 55.71 76.09 218.59 221.72 274.77 TPA 100.97 131.07 99.41 129.01 107.23 134.29 360.39 336.01 Table 4.6 Average Air Fares for Trips from Midway amd O'Hare Airports in Chicago 107 more stop trips, however, show a bit complicated pattern. While on some routes, air fares from O'Hare are cheaper, the O'Hare fares could be 100% or 150% more than the Midway fares on other routes. Meainwhile, O'Hare is offering two-and-more stop flights to more destination airports. This general contrast in fare clearly reflects the status of Midway Airport hosting low-cost carriers. On the contrary, as a primary hub O'Hare offers more flights, more potential connections, serves more destinations with higher flight frequency. We have randomly picked a date, July 14 1999 Wednesday, to check the flight frequencies and lowest one-way ticket price from Midway and O'Hare to three destination Airports. The results are put in Têüsle 4.7. It's clear that O'Hare offers more direct and one stop flights to the three destination airports than Midway. But travelers have to pay more in order to obtain the service and take these advantages. "Hubs provide a different service, it's a different product", as claimed by Northwest spokesman Jon Austin. "It costs more to buy steak than hamburger". Primary airlines can charge travelers more using their strongholds on hubs, where they alone decide where to go, how often to go and how much to charge. Considering the status of O'Hare as a major airline hub, it may not be coincidental to find that some very high fares from O'Hare are associated with certain routes. According to 108 Destination Midway O 'Hare Airports # of # of One Lowest # of # of One Lowest Nonstop Stop One-way Nonstop Stop One-way Flights Flights Fare {$) Flights Flights Fare ($) DCA 1 3 325 28 4 567 lAD 5 6 93 9 30 573 CMH 13 19 92 16 32 167 Table 4.7 Flight Frequencies and Lowest One-way Fares from Midway and O'Hare a survey conducted by USA TODAY, United Airlines controls 61% of the traffic on the Chicago-Washington, D.C. routes, including Dulles Airport (lAD) and National Airport (DCA) . On these two routes, average prices from O'Hare are 81% and 73% higher than the Midway fares. Northwest has frequent flights from O'Hare to its major hub in Minneapolis (MSP) . The average ORD-MSP price is nearly 150% higher than the MDW-MSP fare for direct trips. Other high fare routes from O'Hare involve such destination airports as Cincinnati (hub of Delta), Dallas/Fort Worth (hub of American Airline) . Also, trips ending at airports with large traffic volumes, such as Los Angeles, San Francisco, LaGuardia, tend to have higher prices from O'Hare. 109 Although the status of being an airline huby the size and scope of an airport can not explain all the variations in air fares, they are some important factors we have to consider. It seems that passengers would pay more travelling from and to major hubs, and airports with large volume of traffic. 4.4 Remarks The volatility of airline pricing makes it difficult to present a detailed, fully accurate picture of all of the route-by-route variations. We have tried to pursue some general trends and present illustrative comparisons. While air fares were regulated, resulting in a consistent pattern of air fare change, the current air pricing status is far more complicated and irregular, although a mileage-related fare structure can still be traced. Large fare disparities exist between long-haul and short- haul trips, between direct flights and stopped flights, in the markets of similar stage length, also between flights departing from an airport at various times. It seems that in the current markets short-trip passengers are, to a great extent, in a disadvantageous position. A coast-to-coast flight could turn out to be cheaper than one crossing a single state line. While direct/nonstop trips are more convenient for relatively short origin-destination distances, passengers can 110 save money if they choose connection through another city/airport for their trips with 0-D distances longer than 1,000 or 1,500 miles. Long-haul one stop trips have an edge over both their nonstop and two-and-more stop counterparts. Many major cities in the United States have several airport options, including Midway near Chicago O'Hare, and Providence, R.I., near Boston. Choosing an alternative port may meein a lot lower fare, although low-fare carriers or ports don't have as many flights a day as major airports or fly as many places. Major hub airports provide numerous benefits and convenience for travelers flying from them, but travelers will have to pay in order to enjoy all the services. At a hub where one airline controls most of the gates and runs most of the flights, a passenger may face as high as double-digit price increases. On average, passengers traveling from large hub airports and non-hub airports tend to pay higher air fares, while medium hub airports could offer a better mixture in service and cost. The preceding analyses and findings deal with overall industry-wide averages, as well as some selected routes and ports. Beneath these system averages and samples, there have been a chaotic churning of individual fares, with widely disparate price levels between different markets. Ill CHAPTER 5 AGŒŒGATE PASSENGER ROUTE ANALYSIS The relationship between routing variables (i.e. stopped vs. direct trips), and the variations in air fare have been examined in the previous chapters. In this chapter, a more comprehensive analysis of air passenger routes and the underlying influencing factors will be performed. It has been discussed that in well-functioning competitive markets the desirable rate and quality of production (i.e. air travel services) are determined directly by the interplay of supply and demand. Many factors are involved in a complicated interaction in air passengers' spatial route choice between an origin and a destination. While the analysis of individual factors could provide us with insights into the operation of airline hub-and-spoke network, a comprehensive and multivariate analysis of passengers routes will produce a more complete picture in terms of the interactions among various factors and between demand and supply sides of air travels. The understanding of passengers' 112 preferences and concerns in their spatial route choice is critical for airline industries to adjust their marketing strategies to enhance the competitiveness and profitability in the market. The passenger route analysis in this chapter will be performed at three different levels. The first level of the analysis will be based on trips from a selected origin to all its destinations involving varied types of flights. This level of the analysis can be regarded as an aggregate or macro-level analysis, intending to provide a general picture or pattern in passengers' route choice. The second level of the suialysis is performed for various groups of markets from the selected origin to certain destinations. These groups are formed according to specific attributes of the markets, such as nonstop origin-destination distances. Its purpose is to find out some systematic variations in the air markets at the intermediate level of generalization. Finally the multivariate analysis will be conducted for individual air travel market from the selected origin to each of its destinations. Micro level variations will be examined in these among these markets. 113 5.1 Formulation of Passenger Route Analysis - Theoretical Background According to the theoretical background outlined in the previous chapters, it is generally assumed that the individual choice of a travel alternative, a specific route between an origin and destination in this study, is based on the desire to minimize his general costs and the corresponding inconvenience of the travel, or to minimize his disutility under a series of constraints imposed on him. Also, in the process of seeking higher utility, passengers normally have to make certain trade-offs among different factors and attributes of the alternatives. Under this theoretical framework, our attempt here is to develop a model of aggregate passenger routes considering nonstop flights and various stopped flights generally existing in the airline hub-and-spoke structure. We are trying to express a conviction as to which factors of the route choice or the real situation faced by passengers are central and which are peripheral, based on which some further analysis, such as the trade-off among factors, the elasticity of the choice corresponding to varied factors, could be performed. Generally, models of this type depend upon an explicit formulation of the utility function containing various alternatives' attributes. They differ from each other in terms 114 of a) the particular functional forms chosen for the travel cost utility function, b) the assumptions concerning what variables are assumed to be important and chosen in the function and c) the form of the particular distribution functions hypothesized (Quando, 1970). Meanwhile because of the complications of the problem under study, a specific formulation of the model could differ from another approach by emphasizing on certain aspects of the problem domain. The focus of this passenger route model is on the analysis of the roles played by varied time factors, as well as their interaction with other costs and route attributes, in the process of aggregate passenger travel decision making. The building of the passenger route choice model follows the standard theory of discrete choice analysis. The unit of decision making in this study is a group of persons rather than each individual passenger or traveler because of the limit of available data. It is known that individuals face different choice situations and have widely different tastes in reality. Also, differences may arise among group decision processes because of the variations of within- group interactions that affect the outcomes. Since it is impossible for us to identify the choice of each individual passenger our analysis has to be performed at the aggregate level by assuming some type of homogeneous nature of the 115 passengers in terms of their preferences, tastes and weights on various attributes of the alternatives. Also, as it will be discussed in the following section, the model structure uses only a relatively small number of categorized variables so that the number of cells is also treasonably small in the model specification and calibration. The alternatives considered in this study vary according to the levels of the analysis. For general and aggregate level analysis, three alternatives are considered: direct/nonstop trips, one stop trips and two stop trips. For individual origin-destination pair analysis, all the existing routes including one nonstop, and various one stop and two stop trips are considered alternatives for passengers (e.g. ATL- BOS; ATL-CVG-BOS, ATL-ORD-BOS; ATL-LEX-CVG-BOS, ATL-SDF-CVG- BOS . . . ) . This is actually a multinomial situation with numerous alternatives for each origin-destination pair. For the intermediate level or grouped analysis, the alternatives will be the combination of those for the origin-destination pairs included in each specific group. Generally, these alternatives correspond to discontinuous or discrete alternatives in the theory of choice. The attractiveness of an alternative is then evaluated in terms of a vector of attributes values. These attributes are also considered explanatory variables chosen to explain 116 passenger route structure in air travel. Basically some direct attributes reflecting the service level, or quality of an air transport alternative will be included in the model. Also, some indirect or derived variables would also be calculated using some common factors in the airline operations and included in the model specification and calibration. The decision rule applied in the model building is one of utility maximization. A single objective function expresses the attraction of an alternative in terms of its attributes. Depending upon the nature of this study, this objective function is therefore defined as the general "costs" imposed on air passengers. The notion of trade-offs, or compensatory offsets that a decision maker is using explicitly or implicitly in his comparing different attributes could be derived further from the coefficients associated with these attributes after the model has been calibrated. Generally, we assume that the number of passengers traveling among a set of cities can be approximated by some probabilistic function of a finite set of parameters, and the travel between those cities will primarily depend upon the characteristics of the existing alternatives which connect the origin and destination cities. Adopting the central tenet of the so-called abstract-mode approach (Quando, 1970), the total travel from origin to destination and its distribution among 117 competing alternative routes should be determined only by the attributes of the alternative. This is also true for the share of the travel which is performed by a specific alternative. The predicted passenger travel on each alternative would depend upon the set of characteristics of all the alternatives as long as none of the parameters become equal to zero. So, assuming a vector C represents the general "costs" faced by a passenger when he is making the decision about the choice of a specific alternative i between an origin and a destination, then the corresponding utility function would be = cr ( ) and the utility function for alternative j would be The function U (. ) which maps the attributes values to a utility scale, is an ordinal utility function. Alternative i will be chosen if it is satisfied that 118 5.2 Attribute Variables in Passenger Route Analysis As indicated before, the general costs related to the service quality or convenience of the travel are the most important factors in air passengers' travel decision making. So, selected monetary and time costs, and attributes reflecting the service quality and convenience of air travel are included in the passenger route analysis. Each of these characteristics or attributes associated with different route alternatives typically contribute to the overall "costs" or utility on the route experienced by air passengers. (1) Air Fare The air fare on a route directly reflects the monetary cost faced by a passenger traveling on it. The differences of air fares have a very direct impact on the willingness of a passenger to chose a specific route alternative. The difference in the air fare levels offered by varied airlines on the market is also a very important factor affecting the market competition among them, thus becomes a significant market strategy of an airline. With other attributes or variables fixed, it is expected that there will be an inverse relationship between an air fare level and the participation of passengers, or the market share of a route between an origin-destination city pair (the proportion of passengers traveling on that specific route). It has been noticed that the air fare that an airline is willing to supply 119 on the route could, to some extent, also reflect the operating cost of the airline which in turn is a crucial indicator of economies of traffic density experienced on the route and on other routes. (2) Travel Time Travel time can be regarded as a link characteristic in the airline hub-and-spoke network. Different from direct monetary cost of air fare a traveler has to face to move from the origin to destination, people always feel the inconvenience arising from a long travel distance encountered, a so-called "cost" of distance. In real world travel, the effect or impact of travel distance on a traveler is more likely to be felt or experienced by a passenger in the form of the travel time on the route. The meaning of travel time seems more significant than the travel distance in the route choice of a traveler. A basic linear relationship between travel time and the corresponding travel distance has been determined by empirical studies in the so-called proportional cost approach (Douglas and Miller, 1974; Grove and O'Kelly, 1986). Here the linear function from Grove and O'Kelly is applied in the calculation of in-flight travel time on the route corresponding to an origin-destination distance. The function shows that T = 26.48430 + 0.12189 D (5.1) 120 where T is the total 0-D travel time in minutes, D is the distance in miles. The positive constant could be interpreted as the fixed time required for ground time (i.e. takeoff and landing) (Grove and O'Kelly, 1986) . Generally as a route distance increases (this is especially true for stopped or connecting trips), passengers will have to face more cost of distance, thus the utility corresponding to that route would be expected to decline. (3) Transfer Delay The variable of transfer delay reflects the characteristics of nodes in the air transportation network, or the inconvenience of travel via hub airports. It can also be named "waiting time" for transferring passengers on a stopped route. On arrival at the transferring (hub) airport these passengers will have to deal with some additional delay which arises through the wait between the arrival of the incoming flight from the origin and the departure of the next flight from the hub to the destination. This waiting time at transferring or hub airports is of importance for the airline hub-and-spoke operations which involve many intermediate stops on the way to passengers' destinations. The length of transfer delay or waiting time is a major indicator of the service level of a hub airport and the inconvenience of a stopped flight. The flight frequency and waiting time are closely related since the times of 121 arrivals and departures will determine both. In general, the greater the frequency of either or both services the smaller the waiting time or transfer delay. To reflect the average transfer time delay of a passenger originating on a spoke, we adopted a simple queue framework due to Sen and Marlok (1976) . It is assumed that a passenger tends to minimize his waiting time at the airport, and he will not use those arrival times for which he could make the same departure time by a later arrival. This means that among several consecutive arrivals at an airport a passenger would choose the last one which minimizes the time between the arrival and next available departure. These arrivals are named "selective arrivals". The average waiting time is then the average of the time intervals between every nonredundant departure and the arrival just preceding it. Assuming that there are m (>=1) daily arrivals from the origin to the transferring airport and n (n>=l) daily departures from the transferring airport to the destination, the expected transfer delay or waiting time for a passenger on the route for selective arrivals could then be given by the following equation: WT = 1440 / (m + n) (5.2) where WT is waiting time in minutes considering a 24-hour 122 working day. Since there is big difference in service frequency of inbound and outbound flights among airports, waiting time could, to certain extent, carry the variations across airports in their status. Compared with travel time en route, the effect of waiting time at a transferring airport on the stopped route could be more complicated in its role. Also for those two-stop flights, transfer delay or waiting time will arise at both transferring airports. Daily frequencies from the first hub to the second hub and from the second hub to the destination can be used to figure out the second waiting time. In the passenger route analysis, these transfer delays could be included in the model as individual variables or could be combined to show the overall effect of waiting time on passengers' route choice. (4) Transfer Airport Passengers This explanatory variable is about node characteristics in the hub-and-spoke network. It is used to reflect the activity level of the transferring airports or hubs for stopped routes represented by the passenger count for total traffic in both inbound and outbound directions at an airport, including zero fare passengers. The inclusion of this variable is intending to examine the general impact of hub airports on transferring passengers who have to stop and receive services at these ports. Total passenger traffic to and from an airport is an 123 important indicator of the size and possible functions and services of a hub. The impacts of hub airport activity level could be mixed and complicated. A large and busy hub airport could mean air traffic congestion and possible longer transfer delay, which will negatively affect a passenger's welfare. Passengers, however, may also receive a more complete line of services which can not be obtained at a small and less busy port, such as more frequent flights, and connections and other airport services. This means tha.t the utility of a transfer airport could increase as the level of hubbing rises. From this point of view, the analysis of the factor of airport activity level is expected to be complicated, and the computational results are also expected to be mixed and of wide variations. Again, as the variable of transfer delay or waiting time, for those passengers traveling on a two-stop route, passenger counts of both hub airports will be included in the analysis. It is anticipated that the impacts of these two hub airports could be disparate, too, depending upon the relationships between the two hubs in terms of their relative sizes, functions, and spatial distributions. (5) Number of Stops on a Route This is a variable reflecting the nature of a specific route in terms of the number of stops. It will take the values of 0, 1, and 2 for 124 nonstop/direct routes, one-stop routes and two-stop routes respectively. This variable is incorporated in the analysis to show the difference in the utility of specific route alternative from those of other alternatives when "all the else is equal". It will be able to reflects consumers ' preferences for a specific type of route as well as the degree to which a route alternative is favored or unfavored. A summary of the variables included in the utility function of the passenger route model, as well as the variations among different types of routes, is shown in Table 5.1. Variables Air Fare Travel Transfer Port Number Time Delay Passengers of Stops Nonstop ✓ ✓ n/a n/a 0 Routes One-stop ✓ ✓ ✓ 1 Routes Two-stop ✓ ✓ 2 Routes (2 delays) (2 ports) Table 5.1 Summary of variables in Passenger Route Analysis 125 5.3 The Logit Passenger Route Model and Model Calibration Based on the assumptions and variables discussed above, a multinomial passenger route model of the logit format could be constructed. The route alternatives are as outlined in Section 5.2 for each level of the analysis. The utility functions of alternative routes i and j for a specific origin-destination pair market n is formulated in the following linear-in-the-parameters format: * ^2*^ln2 + '''+ + (5 .3) a n d + (5.4) where x^^k are values of the kth variable for route i and route j in the origin-destination pair n, and (5,, . . ., |3^, are parameters of corresponding variables to be estimated. It's noticed that we have implicitly assumed that the parameters are the same for all passengers in our analysis which is a common practice in the discrete choice analysis. Then the route choice probability or share of passengers traveling on route i for a given origin-destination pair n is expressed as the following logit format: 126 = — ------: (5.5) One of the most widely discussed aspects of the multinomial logit model is the independence from irrelevant alternatives property, or IIA. Stated succinctly, the IIA property holds that the ratio of the choice probabilities of any two alternatives is entirely unaffected by the systematic utilities of any other alternatives. This property is quite important in our passenger route analysis because certain number of alternatives for a specific origin-destination market could be dropped if their passenger volumes are considerably small (see next chapter for detail). IIA property would make us believe that our pursuit of aggregate passenger route structure will not be affected by dropping these routes. The analysis results will remain unbiased and representative. To calibrate this logit passenger route model, we have to consider the limitation of the passenger data which are aggregate in nature (i.e. total passengers are known for each type of route) , not individual passenger-based. It is again convenient to exploit the independence of irrelevant 127 alternatives properties of the logit model. According to Berkson's procedure which is based on the observation that linear-in-parameters logit choice models can be easily transformed to put them in a form amenable to standard regression analysis (Ben-Akiva and Lerman, 1985) . This method which is a well suited to aggregate data and extended by Theil (1969) can be applied to estimate the parameters of the multinomial logit passenger route model. Here, we can simply consider those passengers who travel on a specific route between a given origin-destination pair are in a "homogeneous" group. Each group is assumed to be homogenous in terms of the relationship among the following vectors: = X ^ - Xj„, where J refers to the jth alternative which is used as the reference alternative in the pairwise comparison. Thus there will be varied number of homogeneous groups of passengers for an origin-destination pair depending upon the number of existing routes. The share of passengers choosing route i between a given origin-destination pair can be used as an estimate of the choice probability Let Ri„ and Rj„ denote the numbers of passengers flying routes i and J respectively in origin- destination pair n (n = 1,2, ..., N) . Also, let J be the number of alternative routes available for passengers in origin-destination n and J be a reference route, usually the last one-stop or two-stop route. The logarithms of the "true" 128 odds ratio L “ij between choosing route i and reference route J will be Pj- = log C - p ) (5.6) The logarithms of "observed" odds ratio" are then as the following: -Zw = 1 ^ 1 = n" - (5-7) J a where 3 is a vector of coefficients, is a disturbance term in the regression attributable to the fact that is only an estimate of P^„. It's clear that 5.7 is a system of linear equations. Each origin-destination pair would correspond to (J-1) observations. We have implicitly assumed here that each origin-destination pair consists of passengers with the same route choice set. We would use conventional linear regression techniques to obtain an estimate of 3 in the linear system of 5.7. The main advantages of Berkson's method is that it suits well to the aggregate air traffic data, and allows use of standard regression packages and reduces the number of data points to a relatively small number. It has been shown that 129 this procedure yields consistent estimates of P (Ben-AJciva and Lermany 1985). 5.4 Analysis of Elasticity and Substitution The calibration of the passenger route model would provide us with insights into the decision making process of air passengers in terms of their choice among various available routes.in the airline hub-and-spoke network. The values and signs of variables in the utility function could help to test our hypotheses about the role played by each attribute of the alternatives. One of the important extensions from the calibration of the traffic demand model is the analysis of elasticity, based on which we could do some scenario studies to predict the impacts of the changes of certain variables on the passengers ' routes for a specific origin-destination pair market or the overall airline network. Based on the nature of this study, the analysis of elasticity could be performed in two different levels: individual city pair market level and the overall airline network level. 130 5.4.1 Route Choice Elasticity The route elasticity represents the responsiveness of an individual 0-D market's choice probability between various routes to a change in the value of some attributes. The simplest case is the elasticity of the probability of choosing route alternative i with respect to a change in some attribute that is an independent variable in the model, namely one of the Xinic's. In this case, the direct elasticity of demand model for the nth 0-D market is given by f i dPj- X . = â; - " ITT = [1 - " p.'*1.» (5-»' XnJt * -a Similarly, the individual origin-destination market cross elasticity of the probability route i that is selected with respect to an attribute of route j is f o r j . l (5.9) n Equations in 5.8 and 5.9 can be combined into a single expression (5-101 where 5^^ is a function^ which equals 1 for i equal to j and 131 0 for i not equal to j. From the analyses of these direct and cross elasticities for each individual market, we can exploit the airline market strategies, in terms of the change in the air fare, the improvement in service quality (i.e. frequency), the organization of airline network which could be reflected in the changes in the travel time. Some scenario analyses corresponding to each of these changes could also be performed to investigate passengers' possible responses. 5.4.2 Marginal Rate of Substitution (MRS) and Value of Time It has been pointed out that in the process of a passenger's route choice, he normally needs to make some trade-offs among various attributes of the alternatives. One of the most important such relationships is the trade-off between monetary cost and time spent on the travel. It is not unusual for someone to spend a few more dollars on the air fare in order to shorten his travel time to the destination. Also, in a market with stopped routes, how much more money are people willing to pay for a nonstop flight so that they could avoid the long waiting time at a transfer airport? These questions are especially meaningful and relevant for pleasure travelers who normally book their tickets far in advance and would like to consider the available options and make trade 132 offs. All these issues are related to the general topic of value of time which has been the research focus for a long time. However, for the problem of airline hub- and- spo ke network, it is necessary to investigate more explicitly the value of time when people make their route choice, especially the differences in the implications of time for varied time factors, such as travel time en route, and waiting time at the transfer point. Here, the concept of marginal rate of substitution (MRS) from the logit passenger route model could serve this purpose. The basic idea of marginal rate of substitution is that if an attribute for a route alternative (e.g. travel time) changes by an small amount, how the other factor (e.g. travel cost) must be changed in order to keep the choice probability constant. For an 0-D pair market n, the MRS between the kth and the 1th attribute corresponding to route alternative i would be given by the following expression sp.Vax a iaJc With linear in parameters in the utility function. Pi 133 With k representing the cost factor, and 1 representing travel time, schedule delay or waiting time respectively, we would be able to derive the values of these different time factors in the aggregate passenger route structure. 5.5 Summary In this chapter, the construction of a logit format, multivariate and multinomial passenger route model is outlined. The theoretical background of the model formulation is the discrete choice theory and the notion of consumer/passenger welfare maximization in transportation. Explanatory variables included in this model tend to reflect the basic characteristics of airline hub-and-spoke networks. Direct monetary cost and time cost are incorporated in the analysis, and both link and node characteristics of the airline network are specified in the model. Considering the aggregate nature of passenger data, the Berkson-Theil estimation method which transforms the route data and utilizes the conventional linear regression techniques is applied in the calibration of the route model. The extension of the logit route model could help obtain route elasticity and marginal rate of substitution (MRS), from which passengers' response to changes in route characteristics and values of various time factors could be derived and analyzed. 134 The results of empirical data analysis involving various destinations and numerous routes with Atlanta being the origin and focus will be presented in the next chapter. 135 CHAPTER 6 RESULTS OF EMPIRICAL PASSENGER ROUTE ANALYSIS In this chapter, the logit passenger route model developed in Chapter Five will be applied to empirical USDOT 10% O&D passenger survey data. Atlanta is selected as the origin for all the routes used in passenger route analysis. The characteristics of Atlanta-based route structure will be examined first, following which the results of the calibration of passenger route model will be presented and discussed. 6.1 Characteristics of Atlanta-Based Routes Atlanta was chosen among the thirty one airports as the origin for all the routes considered. We select Atlanta as the origin and focus of the route choice analysis based on the following considerations. First, Atlanta is an important passenger hub airport (major hub of Delta Airline) , and it ranked the tenth among 4 00 airports in the United States in 1997 in terms of the total inbound and outbound passengers. Also, it reinked the fourth in terms of the departure and 136 arrival of passengers in our database containing 31 major airports in the continental United States. Because of the large volume of passenger flows from Atlanta, the number of existing routes to twenty-nine destination airports^ is sufficient to provide a large pool of routes for passenger choice analysis. Second, compared with other thirty airports in the database, Atlanta is in an advantageous location with respect to the spatial distribution of airports. This favorable location leads to a large range of city-pair distances from Atlanta. There are both short-haul routes and long-haul, cross-continental routes from Atlanta. For example, the nonstop city-pair distance from Atlanta to Memphis, TN (MEM) is only 332 miles, while the city-pair distance between Atlanta and Seattle (SEA) is 2,182 miles. This large range of distance would help analyze passenger choice at certain grouped or aggregated levels (e.g. grouping origin-destination routes according to nonstop distances) . In the 10% 0-D survey database, there is a larger number of routes from Atlanta to the twenty nine destination airports containing only extremely small passenger volumes. In order to choose only those important routes and show general and representative Atlanta-based route structure, a threshold has DAL is dropped because of its extremely small total passenger flow from Atlanta. 137 to be determined to exclude those routes with small passenger flows. Here, we choose 100 as the minimum annual passenger flow for a route to be included in the investigation. The result shows that nearly 99 percent of all the Atlanta-based passengers are included in the analysis, among which there are all of the direct passenger flows, 97.5 percent of total one- stop passenger flows and 33 percent of total two-stop passenger flows. We believe that this sample of data containing 566 available routes^ well represents the distribution of passengers among various routes from Atlanta. Nonstop 0-D distance and number of different types of routes from Atlanta to the destination airports are summarized in Table 6.1. Figure 6.1 through Figure 6.26 delineate detailed route structure from Atlanta to each of the twenty nine destinations^. Please refer to i^pendix B for the codes of all intermediate stop airports and cities. 3 Please pay attention to the following aspects of route maps: (1) the origin and destination airports are distinguished from intermediate stops by being labelled with large, italic-bold font; (2) only links from the origin to intermediate stops are shown, and links from intermediate stops to the destination are omitted for clarity purpose; (3) one-stop routes and two- stop routes are depicted on separate maps for those destinations with large number of each; (4) the best way to trace a route is to begin at the origin (ATL) , following ___ links to find stop airport for one-stop route, and following links to locate the first and the second intermediate stops for two-stop route. 138 Destination Nonstop O-D Number of Routes Airports Distance Nonstop One-stop Two-stop (miles) MEM 332 1 3 — — CVG 373 1 27 ----- TPA 406 1 9 ----- MSY 425 1 3 -- STL 483 1 6 — PIT 526 1 9 4 lAD 533 1 9 1 DCA 547 1 16 2 CLE 554 1 10 6 BWI 576 1 10 3 MDW 590 1 5 ----- DTW 594 1 13 3 MIA 596 1 9 1 OEU) 606 1 22 9 PHL 665 1 17 6 lAH 689 1 6 ----- MCI 693 1 9 2 HOU 696 1 4 — DFW 732 1 29 — EWR 745 1 22 7 JFK 760 1 17 1 LGA 761 1 26 4 Table 6.1 Atlanta (ALT)-Based Route Structure (continued) 139 Table 6.1 Atlanta (ALT) -Based Route Structure (continued) MSP 906 1 14 2 BOS 946 1 24 3 DEN 1199 1 14 7 PHX 1587 1 16 4 LAX 1946 1 31 24 SFO 2139 1 22 20 SEA 2182 1 19 7 Total 29 420 117 It can be seen from the figures in Table 6.1 that among the passenger flows to the twenty nine destination airports, the number of routes and types of routes are highly unevenly distributed. For instance, Los Angeles (LAX) has totally fifty six routes while Memphis (MEM) and New Orleans (MSY) each have only four routes with sufficient passenger flows above the threshold. In general, large passenger airports located in the massive metropolitan areas and those airports which are major airlines' hubs, such as San Francisco (SFO), O'Hare (CRD) in Chicago, Newark, NJ (EWR), LaGuardia (LGA) in New York, Dallas Fort Worth (DFW), and Cincinnati (CVG), receive frequent 140 flights and have more routes available from Atlanta. Also, these destination airports are generally at a medium or long nonstop distance from Atlanta. Table 6.2 summarizes the total passengers to the twenty nine destination airports, as well as the passenger distribution among nonstop, one-stop and two- stop routes. While overall the nonstop flights dominate routes originating from Atlanta, some variations among the twenty nine destinations can still be identified. Five "nearby” airports including CVG, MEM, MSY, STL and TPA do not support two-stop routes from Atlanta with significant passenger flows. These are basically short-haul routes, with nonstop distances shorter than 500 miles. On the contrary, the three cross continental trips with the longest nonstop distances, to Seattle, San Francisco, Los Angeles, have larger number of passengers traveling on two-stop routes. While the overall proportion of two-stop trips from Atlanta to the twenty nine destination airports is less than 0.4 percent, these three airports support 1.1 percent, 1.7 percent, and 1.6 percent two-stop passengers respectively. Some interesting points can also be made regarding the geographical variations of the Atlanta-based route structure. The intermediate stop airports for destinations in the northeast are generally distributed east of St. Louis and Memphis with St. Louis being the west most (Figures 6.6, 6.7, 141 Destination Passengers Airports Direct One-stop Two-stop Total EWR 58317 1228 148 59693 ORD 51004 1298 146 52448 DFW 45552 1989 --- 47541 MIA 36915 355 13 37283 TPA 36462 254 --- 36716 PHL 32599 1068 181 33848 LGA 29107 3125 82 32314 LAX 21059 10156 503 31718 BOS 21968 6354 66 28388 DTW 25674 915 32 26621 lAD 23454 2142 26 25622 SFO 17209 6431 408 24048 MEM 21530 63 --- 21593 DCA 17854 3007 25 20886 MSY 18384 468 --- 18852 DEN 14902 2797 146 17845 BWI 16497 453 78 17028 lAH 13294 1306 23 14623 PIT 14070 307 45 14422 MCI 11980 1536 41 13557 MSP 12219 1190 30 13439 PHX 10568 2309 70 12947 Table 6.2 Route Passengers (10%) From Atlanta (continued) 142 Table 6.2 Route Passengers (10%) From Atlanta (continued) SEA 6984 5001 133 12118 STL 11301 718 -- — 12019 JFK 10200 574 16 10790 CVG 9026 1517 --- 10543 CLE 9488 759 106 10353 MDW 9325 420 ------9745 HOU 3717 567 --- 4284 Total 610659 58307 2318 671284 6.9, 6.13, 6.17-19, 6.21). Except for Washington, DC area, those northeastern destinations also have numerous detour or circuitous routes with many intermediate stops south of Atlanta in Florida such as TPA, MCO, FLL, and MIA. Meanwhile, Cincinnati (hub of Delta Airline) emerges as a key airport on both one-stop and two-stop routes. It is the second stop on almost all the two-stop routes, while SDF, LEX are generally the first stops. Another cluster of airports which are important for two-stop routes will be in North Carolina and South Carolina, including Charlotte (CLT), Greensboro (GSO). In general, routes to the north and near northwest 143 destinations are distributed north of Atlanta (Figures 6.2, 6.4-5, 6.8, 6.10, 6.12, 6.15, 6.20). We can again notice the key position of Cincinnati on those routes. Cincinnati has a large number of one-stop routes from Atlanta. Meanwhile, most of the two-stop routes go through this airport. For example, it is the second stop of all the two-stop routes ending at Chicago O'Hare Airport. The overall passenger volume on routes via Cincinnati is also large. Charlotte is another important port on many two-stop routes. Comparatively, the route structure to destinations west, especially far west of Atlanta is more complicated, in terms of the number of routes and connections (Figures 6.16, 6.22- 26) . Several airports emerge as the important ones on those routes with Dallas Fort Worth (DFW) being the most notable. Other dominant ports include Denver (DEN) , Salt Lake City (SLC), Chicago O'Hare (ORD), and Phoenix (PHX). These ports play a significant role serving both one-stop and two-stop routes in the market west of Atlanta. 144 STL BNA é: MEM lAir MS ROUTES TO MEM MCO ROUTES TO MSY FIGURE 6.1 ONE-STOP ROUTES FROM ATLANTA (ATL) TO MEMPHIS, TN (MEM) AND NEW ORLEANS (MSY) p * MKE( DTW, FWA( 'CR' STL' ROU MEM, BHM UTL FIGURE 6.2 ONE-STOP ROUTES FROM ATLANTA (ATL) TO CINCINNATI (CVG) )CSO MEM A m PNS TPA MIA FIGURE 6 J ONE-STOP ROUTES FROM ATLANTA (ATL) TO TAMPA (TPA) DTW, ORD MCI STL ,c l t 00 MBMi ’ATI FIGURE 6.4 ONE-STOP ROUTES FROM ATLANTA (ATL) TO ST. LOUIS (STL) DTW. ORD PIT m 'JFK BNA ATL ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6^ ROUTES FROM ATLANTA (ATL) TO PITTSBURGH (PIT) DTW. PIT SDJi U» O - ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.6 ROUTES FROM ATLANTA (ATL) TO WASHINGTON DULLES AIRPORT (lAD) BOS DTW prr CV( STL' RDU Ul MEM ONE-STOP ROUTES MCO TWO-STOP ROUTES FIGURE 6.7 ROUTES FROM ATLANTA (ATL) TO WASHINGTON NATIONAL AIRPORT (DCA) DTW, ORD CVO STU' N(BM( TL ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.8 ROUTES FROM ATLANTA (ATL) TO CLEVELAND (CLE) DTW, IWR CVO' STL -HF u» w f 'C L T CHS ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.9 ROUTES FROM ATLANTA (ATL) TO BALTIMORE WASHINGTON (BWI) MSP DT\ PIT CVO STL % MEMi ATL ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.10 ROUTES FROM ATLANTA (ATL) TO DETROIT (DTW) ORD P.T BNA <-nLA .TUI PNS (AX ONE-STOP ROUTES tpaT TWO-STOP ROUTES m i A FIGURE 6.11 ROUTES FROM ATLANTA (ATL) TO MIAMI (MIA) MSP CLJ PIT ICA WCl STU MBM BUM MSY MCO FIGURE 6.12.1 ONE-STOP ROUTES FROM ATLANTA (ATL) TO CHICAGO (ORD/MDW) LA \ATL FIGURE 6.12.2 TWO-STOP ROUTES FROM ATLANTA (ATL) TO CHICAGO O HARE AIRPORT(ORD) DTW, CL£ ORI STL V/l 00 MEM :a e ONE-STOP ROUTES MC( TWO-STOP ROUTES FIGURE 6.13 ROUTES FROM ATLANTA (ATL) TO PHILADELPHIA (PHL) ORD STL nJL, .CLT LA VO MEM 'ATL lAH/HOV ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.14 ROUTES FROM ATLANTA (ATL) TO HOUSTON (lAH/HOU) cm ORD CVOI MCI STL ,CLT MEM, ATL DFW. ONE-STOP ROUTES lA ll TWO-STOP ROUTES FIGURE 6.15 ROUTES FROM ATLANTA (ATL) TO KANSAS CITY, MO (MCI) DEN, CVO STL jCLT o\ OKC, MEI jn DFW • SHVl AUS, BTR( SAT [AH/HOU RSW FIGURE 6.16 ONE-STOP ROUTES FROM ATLANTA (ATL) TO DALLAS (DFW) BOS DUF DTW, EWR LD/MD^ STL' s MBM rSAV - ONE-STOP ROUTES iMCO TWO-STOP ROUTES TPA FIGURE 6.17 ROUTES FROM ATLANTA (ATL) TO NEWARK, NJ (EWR) BOS BUF DTW ORD' PIT /™ L STL CLT ONE-STOP ROUTES TPA TWO-STOP ROUTES FLL MIA FIGURE 6.18 ROUTES FROM ATLANTA (ATL) TO JFK AIRPORT, NY (JFK) ROC BOS DTW 'ORD PIT puli' VC SDf, STL ,OSQ MEM CHS SAY ONE-STOP ROUTES TPA TWO-STOP ROUTES PBI RSW" I FLL MIA FIGURE 6.19 ROUTES FROM ATLANTA (ATL) TO LAGUARDIA AIRPORT, NY (LGA) MSP MSN DTW !WR DMA m 0\ CLT Ul MEM ATi DFW, lAH ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.20 ROUTES FROM ATLANTA (ATL) TO MINNEAPOLIS (MSP) \os DTW, ORI CVi STL’ MEM ONE-STOP ROUTES PBI TWO-STOP ROUTES MIA FIGURE 6.21 ROUTES FROM ATLANTA (ATL) TO BOSTON (BOS) MSP, DTW PIT IN D 1 t f W l COS STL CLT MEI ATI JAN. ONE-STOP ROUTES IA 11*1 TWO-STOP ROUTES FIGURE 6.22 ROUTES FROM ATLANTA (ATL) TO DENVER (DEN) MSP, DTW sue DEN VO iDFj COS s PHX 'MBM TUS, ATÙ DFW ONE-STOP ROUTES ISY TWO-STOP ROUTES FIGURE 6.23 ROUTES FROM ATLANTA (ATL) TO PHOENIX (PHX) SBA MSP DTW. CI,B SLC ORD PIT PHI SPC DHN CVOl COS STL US ONT s [SAN PHX, TUS DFW MSY ICO TPA MIA FIGURE 6.24.1 ONE-STOP ROUTES FROM ATLANTA (ATL) TO LOS ANGELES (LAX) PJW DSM LOA / SLC ORD DBN STL ■S-/ PHX MBM. Lf^ri •tfUM DFW,' JAN SAT FIGURE 6.24.2 TWO-STOP ROUTES FROM ATLANTA (ATL) TO LOS ANGELES (LAX) MSP DTW iBWR ORDi SFO SLC VO DEN STL COS iMRY LAS JCLT I SNA Urt MIX DFW lAH FIGURE 6.25.1 ROUTES FROM ATLANTA (ATL) TO SAN FRANCISCO (SFO) ONE-STOP SLC, SFO ORD LA§. ,SAN HSV fATi DFW, MSY' SAT FIGURE 6.25.2 TWO-STOP ROUTES FROM ATLANTA (ATL) TO SAN FRANCISCO (SFO) SEA PDX MSP DTW SLC, PIT SFO ORD DHN COS LAS STL LAX .CLT w PHX ATi DFW lAH, ONE-STOP ROUTES TWO-STOP ROUTES FIGURE 6.26 ROUTES FROM ATLANTA (ATL) TO SEATTLE (SEA) 6.2 Discussion of Passenger Route Model The logit-based passenger route analysis is performed at three different levels. First, at the grand aggregate level only the total number of passengers on direct, one-stop, and two-stop routes are considered for each origin-destination market. No detailed individual routes are recognized. Three explanatory variables - TravelTime, Fare and Number of Stops - are included in the analysis. The pairwise logarithmic transformation (equation 5.7) is performed among the three routes, thus each origin-destination market will generate two observations. Second, at the combined level all the available, individual routes for a specific origin-destination market are recognized. Passenger flows on each route and the attributes of each route for every 0-D pair are used. Pairwise logarithmic transformation (equation 5.7) is again performed among the available routes for every origin-destination market, thus a specific 0-D market with J available routes will contribute J-1 observations to the model calibration. All the observations from each origin-destination pair are combined together to estimate parameters. Origin-destination markets are then organized into five groups according to their nonstop distances from Atlanta. The same procedure just discussed for combined data is applied to each group to 174 generate observations for parameter estimation of each distance group. Finally, parameters are estimated at individual market level for each origin-destination pair. Because some 0-D markets have too small number of routes available to generate sufficient observations to perform regression-based parameter estimation, only 23 origin-destination pairs are eligible for parameter estimation at individual market level. Estimated parameters at grand aggregate level are presented in Table 6.3. At this aggregate and general level, all of the three variables - TravelTime, Fare and Number of Stops - have negative coefficients as expected. It indicates that in general with the prolonged travel time en route, the rise of air fare and the increase in the number of stops the probability of passengers choosing air travel will be lowered. People do prefer direct routes without intermediate stops. Stopped trips, if without other incentives such as lower fare, will discourage the patronage of travelers. Tables 6.4.1 and 6.4.2 summarizes the estimated parameters at combined data level. The difference between the two tables lies in the variable TotTransferTime which is the sum of Trans f erTime 1 and Trans f erTimeZ at two transfer airports for two-stop routes. As at aggregate level, the coefficients of Fare, TravelTime and Number of Stops are all 175 SUMMARY OUTPUT Régression Statistics TravtlTinw Far* #Stop MultiptoR 0,849266147 1 2 3 R Square 0,721252988 Adjusted R Square 0.692934916 Standard Error 0,896833592 Otwervations 58 ANOVA d f SS MS F Significance F Regression 3 114,4626587 38,15421956 47.4371775 3.73466E-15 Residual 55 44,23707702 0,804310491 Total 58 158,6997357 Coetncients Standard Error IStat P-valua Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0 #N/A #N/A «N/A «N/A «N/A #N/A «N/A X Variable 1 •0,015371084 0,004783043 -3,213662054 0,00219355 -0,024956515 -0,00578565 -0,02495651 -0,00578565 X Variable 2 •0,005828297 0,001834995 -3,176192995 0,00244685 -0,009505706 -9,00215069 -0,00950571 -0,00215089 X Variable 3 -1,539701343 0,200334809 -7.685640609 28426E-10 -1,941181173 -1,13622151 -1,94118117 -1,13822151 Table 6.3 Estimated Parameters of Logit Passenger Route Model (Aggregate Data) SUMMARY OUTPUT Reafosskjn Statistics Fare TraeaiTima TransiarTiinal TiansMrTimaZ F o rti Fert2 • •tap MuWptoR 0.7M 69S912 1 2 3 4 8 6 7 RSquare 0.634724376 Adjusted R Square 0.628621788 Standard Enor 1.188454531 Obsenations 530 A I40VA d f SS MS F Sianilicanoa F Regression 7 1283.604754 183.37211 129.8279307 5.5352E-110 Residual 523 738.6978426 1.4124242 Total 530 202 Z 30 2 596 CoafUciants StandardErior ts ta t P -v a k » (jO w sr95N Ueoaras% U*ar@50MU0dar950M Intercept 0 WH/A tm iA •MIA •MIA •MIA •MIA •NIA X V a r ia i* 1 4)005447005 0.000520256 -10.469858 2.03676E-23 4)006469052 -0.004425 41.0064691 4 )0 04425 X V ariab ie2 4)009532793 0.001376948 -6.9231326 1.29924& 11 4)012237818 4)0068278 4 )0 1 2 2 3 7 8 4 )0068278 X V ariabie3 4)008752934 0.001303242 ■6.7162771 4.87404E-11 4)011313163 4)0061927 4 )0 1 1 3 1 3 2 4 )0061927 X Variai* 4 0.014705916 041018371 8.0049613 7.779 09 & 1 5 0.011096917 0.0183149 0.0110969 0.0183149 X V a r ia i* 5 8.85097B 088.07429G O 8 1.0961913 0.273499571 -7.0110554)8 2 4 7 15 4 )7 -7 .0 1 1 5 0 8 2 4 7 1 5 0 7 X V a r ia i* 6 6.071 37 E 07 9.56233E418 6.3492516 4.70024E-10 4.1928454)7 7 .9 5 5 0 7 4 .193 54 )7 7 .9 5 5 0 7 X Variable 7 -2.945740257 0.186314398-15.810637 2 6 2 2 0 9 5 4 6 .3.3117W486 -2579733 -3.3117655 -2579733 Table 6.4.1 Estimated Parameters of Logit Passenger Route Model (Combined Data) SUMMARY OUTPUT Raarossion Statistics Fare TravefTlirw TatTtenstarTbne Forti FortZ • • t a p MuRipieR 0.732957301 1 2 3 4 8 • R Square 0.537226405 Adjusted R Square 0.53090223 Standard Error 1.336416139 Observations 530 ANOVA d f SSMS F SmnrtkanceE Regression 6 1086.434354 181.07239 101.3838587 2 3 9 93 3 5 -8 4 Residual 524 935.8682419 1.7860081 Total 530 2 0 223 0 2 5 9 6 Coattidants Standard Error ts ta t F>-vakjB Lomr 95% Upoar9S% Lamer9S.0%Uppar95.0% Intercept 0 •N/A •N/A •N/A •N/A •N/A •N /A •N /A X Variable 1 4)006319018 0.000579161 -10.910642 4 .0 8 1 7 6 5 2 5 4)007456779 4)0051813 0.0074568 0.0051813 X Variai* 2 4)008480497 0.001545188 -5.4883285 6 .3 2 8 8 9 5 0 64)011516017 4 )005445 0.011516 0.005445 X V a r ia t* 3 4)002012939 0.001317675 -1.5276447 0.127204143 -0.004601512 0.0005756 0.0046015 0.0005756 X Variable 4 -26069754)8 9.0140154)8 41.2892133 0.772532552 -2.031554)7 1 .5 1 5 0 7 -2031507 1.51507 X V a r ia i* 5 3 .8 3 6 8 5 5 0 7 1 .0 5 4 2 3 5 0 7 3.639476 0.000300273 1 .7 8 5 8 1 5 0 7 5 .9 0 8 5 0 7 1.766507 5.908507 X V a r ia l* 6 -1.710576441 0.173500264 -9.8592152 3.783575-21 -2051417615 -1.3697353 20514176 -1.3697353 T«d)le 6.4.2 Estimated Parameters of Logit Passenger Route Model (Combined Data) 177 negative, and these are all significant variables statistically, which implies that the impacts of these variables are pretty consistent when all the individual routes are considered. Transfer (waiting) time, if entered as a total of the transfer times on two legs of two-stop routes, has a negative sign. However, its role is not quite significant. If entered as separate variable for each leg, TransferTimel (at the first stop airport) is significant and has a negative sign. Comparatively, the transfer time at the second stop airport is less significant and has an unexpected positive sign. Similar to the situation of transfer time, the impacts of the two connecting airports, in terms of their activity level, are also varied. The second connecting airport is basically significant and positively signed, while the first connecting port is insignificant. Generally a crowded airport with heavy passenger traffic could lead to a longer potential delay and longer waiting time, thus negatively influencing passengers' welfare. But variation in roles can be found between the two intermediate airports on Atlanta-based two- stop routes. Considering those dominant second connecting airports, such as Cincinnati (CVG), Dallas Fort Worth (DFW), Denver (DEN), O 'Hare (ORD), Salt Lake City (SLC), we can see that they are all principal large hub airports which offer frequent flights to a large number of destinations. So, it 178 will not be very surprising that travelers may be willing to stop at these airports on the way to their destinations, although this may mean a relatively longer transfer or waiting time at these airports. The waiting time can be justified by the opportunities provided by these large hubs, and the waiting is worthwhile. The results of parameter estimation for distance-based route groups are shown in Tables 6.5. As before. Fare, TravelTime and Number of Stops are consistently negative in their coefficients and generally statistically significant. TransferTimel is also negative in sign while TransferTimeZ at the second connecting airports is positively signed. The effects of the activity level of connecting airports are quite varied, which implies that the impacts of connecting hub airports are highly mixed. They could influence passengers' welfare in both positive and negative manner, and a single generalized measure will be very difficult to obtain. Some other variations among different distance groups are also worth notice. First, on average the absolute magnitude of the coefficients of Fare for short or medium-ranged routes is bigger than that for long-haul routes. This could mean that passengers are more sensitive to the fare change for short- distance travels. Generally there will be more options existing for relatively short travels. Cheap fare will 179 Non-Stop Distauice Range < 500 mi 500-1000mi 1000-1500mi 1500-2000mi > 2000mi Fare -0.006895975 -0.004633236 -0.006629062 -0.003903157 -0.002544375 TravelTime -0.008966055 -0.009543351 -0.020281798 -0.009500667 -0.016801533 TransferTimel -0.003740211 -0.02035962 -0.012718001 -0.004907461 -0.001746977 TransferTime2 N/A 0.012298875 -0.034612926 0.006476691 0.014209683 Portl 9.03078E-07 -4.29552E-07 6.25813E-Q7 5.39201E-07 4.40864E-07 Port2 N/A 2.85173E-07 7.7273E-07 8.33756E-07 1.50862E-07 # Stop -5.856533121 -2.307896478 -4.236749933 -3.29571643 -3.176538735 R Square 0.879495471 a.€65392157 0.873154692 0.704728398 0.818268012 Table 6.5 Estimated Parameters in Logit Passenger Route Model for Groupd Data 180 encourage passengers to fly while an expensive air fare will make passengers keep an eye on other alternatives. For long distance trips, especially those more than 1,500 miles, basically air travel is the only option leaving people with limited choices in response to the fluctuation of air fare. Second, the impact of the variable of Number of Stop is the largest for under-SOOmile routes, and it becomes less influential for long distance travels. People prefer direct or one-stop routes and discard two/more-stop routes for short trips. They will be willing to accept stopped routes for long distance travels in which prolonged travel time may not be that critical in comparison with the already quite long iourney. Finally, the estimated parameters for each individual origin-destination market are compiled in. Table 6.6. This table exhibits some more complex irregularities in estimated variable coefficients, especially for two transfer times and two stop ports. Differences could be noticed not only among markets with various distances, located in different regions of the country, but also among markets in the same region with similar distances. So, the generalization is even more difficult to be made looking at the level of individual origin-destination markets. This may again prove the complexity of the essence of airline hub-and-spoke operations. 181 D M ttM N M murnmp M l O taS P M T w m lT I— T w H M T fe a a l T l— M T I — » P w tl P M t • • M m # 8 M # cvo 373 .0.003075529 4)003192824 4)011182903 80888064)7 -4.5705508130.(41018014 TFA 406 .0000450739 4)001843315 0.002430082 95802154)7 -81284808880 0 8 9 4 9 (9 (8 PIT 526 .0.004782477 4)024008800 4)010880881 0.023818173 2.7752864)7 2 0 8 3 5 1 6 0 8 -4.534710872 0 0 7 (7 7 7 0 7 0 lA D 533 .0.020834115 4)111395783 0.047037238 4)047808085 4 .1 5 2 2 7 6 0 8 2 8 7 3 8 3 6 0 5 -3782130007 0 0 4 4 5 5 0 3 OCA 547 41.008837802 4)017538105 4101359018 0.023807000 2.5518564)7 190055608 -4.4550124770 ( 5 3 0 8 ( 8 ( 7 OLE 554 41008073292 4)029020819 4)0404044830.007514725 .5.68390608 7.7111607 -1.500752819 0 (1 7 5 4 2 8 5 1 wm 576 4100884051 4)000750748 41023181035 0.023887485 -1.02817608 -5.8800864)7 -3817414855 0 0 8 0 2 7 3 0 5 1 DTW 584 4)002880742 4)014304878 4)0010153540.095018319 9.80182607 -1.0790608 -6.580003780 0 2 0 1 7 1 0 0 7 M A 595 41008503753 41008320223 4)008581311 3842953821 382008607 -372385605 -8.0088274230 0 7 8 1 1 7 4 8 OKD 608 4)004313825 4)008731848 4)0161245830.130662002 -8.0807264)8 -8.41174608 -5.2053482330 (0 5 0 1 4 5 8 5 PHL 665 4 1 0 0 3 8 2 2 0 4 4)015020803 4)023098080 0.020548422 -1.01000608 248554608-29834403850 8 0 3 0 0 3 1 2 4 MCI 693 4)000805825 4)005320823 4)0187030884)018203087 31025864)8 302855608 -31341808850.001711473 D fW 7 32 4)004058272 4)008012453 4)021078804 -1.80531607 -4.3858374320.821318713 iwm 745 4)0044588154)000443934 4)022412538 0.027978028 -8.490226078.1583064)7 -30782578020 8 7 5 3 3 0 8 5 5 JP K 760 4)0035822554)001292207 0.007481082 0.015381283 382838607 8.58857608 -8.7225418070 0 0 3 2 3 7 5 2 5 LOA 761 4)008285083 4)009047358 4)0131773844)328996802 4.07284607 1.27878605 -5.1537931840.(08247402 MSP 906 4)008338821 4)017955249 4)019283203 0.020401011 3 9 7 9 8 5 6 0 7 3 3 5 0 4 6 0 7 -4.342111110 0.034030785 ■O S 946 4 )0 0 7 4 3 2 4 1 4)017752193 4)015437738 4)024508202 4 .7 8 4 9 9 6 0 7 1.00355605 -4.431485183 0(384027(8 OEM 1199 4)0088298824)020281798 4)012718001 4)034812028 8 .2 5 8 1 5 6 0 7 7 .7 2 7 3 6 0 7 -4.2387409330 (7 3 1 5 4 8 0 2 PHX 1567 4)011598218 4)010817827 4)008877824 0.009562621 88175864)7 1 75201608-4 5857820480 (4 8 9 7 (9 8 1 LAX 1946 4)003542077 4)012984498 4)003879483 0.004018405 4.8357964)7 4.93184607-2947803521 0.701(92482 SPC 2139 4)004878507 4).020111517 4)003407077 0.015552271 318574607 283398607-3.888807218 0.833142518 SEA 2182 4)003184285 4)015908184 4)000155445 0 01 1 0 70 5 5 5 .8 8 5 4 2 6 0 7 7 8 5 9 8 7 6 0 7 -3.4249853470.7(1062942 Table 6.6 Summary of Estimated Parameters of Logit Passenger Route Model for Individual 0-D Pairs 182 as well as the passenger choice in this complicated network. On the other hand, however, what we have examined and discussed in previous paragraphs is to a large extent still valid. The individual 0-D market level variation pretty much echoes those identified at combined and grouped levels. 6.3 Analysis of Elasticity and Marginal Rate of Substitution As described in Chapter 5, the elasticity for an origin- destination market represents the responsiveness of the choice probability among various routes to a change in the value of some attributes. In this section, we will analyze two types of elasticities - direct elasticity and cross elasticity. Specifically we will use direct elasticity to examine the change in probabilities choosing direct, one-stop and two-stop routes, in response to the changes of travel time and air fare respectively on each route. Using cross elasticity, we try to analyze the change in the probability choosing stopped routes in response to the change in travel time and air fare on the direct route. Table 6.7 summarizes the direct and cross elasticities for each origin-destination market. It can be seen from the table that in general, with the increase of either travel time or air fare on a route, the probability of passenger choosing that route tends to be lowered. Also, the elasticity of travel time for each route is basically lower 183 Destination Direct Elasticity Cross Elasticity F a re T ra v e fn rn e F are T ra va m m s Direct 1-Stop 2-Stsp D ra c t 1-Stop 2-Stop MEM -0.0449 -1.3606 -Z2977 -0 .0 1 6 3 -1.5601 -1 .4 8 3 8 0.9823 0.3573 CVG -0 .1 1 2 3 -1.5179 -3.1251 -0.0982 -0.9543 -1 .7 1 2 6 0 .9 9 1 8 0.8761 TPA -0.0900 -1.5130 -Z 0 6 3 3 -0 .0 3 5 6 -0.9176 -1.8920 1 .0 7 5 9 0.4261 MSY -0.0664 -1.9608 -Z9358 -0.0407 -0 .7 1 2 2 -0.9409 1.1170 0.5260 STL -0.1689 -1.6828 -Z 0 2 1 4 -0 .1 2 9 3 ■0.7454 -1.7263 1 .1 4 1 3 0 .8 7 3 7 PIT -0 .1 0 6 0 -1.7914 -1.9485 -0.0457 -1.0847 -1.4880 1.2847 0.5535 lA O -0 .2 2 4 6 - 1.3271 -Z 6 6 2 4 -0.0977 -0.5122 -1.2301 1.1811 0 .5 1 3 8 DCA -0 .3 4 1 0 -1.3834 -1.8911 -0 .3 0 8 6 -0.4914 -1.5949 1.0890 0.9656 CLE -0.2132 -1.7032 -1.7956 -0.1517 -0 .7 8 7 5 -1 .1 6 6 3 1.2300 0.8752 BWI -0.1962 -1.6993 -1 .7 6 6 1 -0.1229 -0.9157 -1.1798 1.2882 0 .8 0 7 3 M DW -0 .1 6 1 6 -1 .8 0 5 3 -Z 2 5 4 7 -0.0487 -0.5251 -1 .8 7 2 7 1.3509 0.4068 DTW -0.1459 -1.7914 -Z2497 -0 .0 5 9 7 -0 .8 5 8 2 -1.7277 1.3723 0.5615 M IA -0.1 17 1 -1 .8 4 1 4 -Z 4 2 3 6 -0 .0 4 2 5 -1.0327 -1.6392 1 .4 0 4 7 0 .5 0 9 2 ORD -0.1220 -1.9038 -Z2425 -0 .0 4 5 9 -0.9957 -1.6553 1.4186 0.5334 PH L -0 .1 6 6 3 -Z0182 -1.9788 -0.0649 -0.8232 -1 .2 9 8 5 1.4867 0.5803 lA H -0 .2 2 9 4 -1 .9 7 9 0 -Z 2 7 2 9 -0.1492 -0.8292 -1.7077 1.4667 0.9537 MCI -0 .2 2 4 1 -1.8503 -Z3663 -0 .0 9 0 6 -0.6371 -1.3183 1.4814 0.6000 HOU -0 .2 5 6 6 -1.9122 -Z6308 -0.1 72 1 -0.7191 -1 .9 6 2 0 1.4506 0.9657 D FW -0 .1 3 4 7 - Z 1 1 3 9 -Z 4 8 8 9 -0 .0 5 3 7 -1 .1 7 6 9 -1.8442 1.6420 0 .6 5 4 5 EWR -0 .1 6 0 0 -Z 1 7 5 6 -Z 2 4 4 1 -0.0666 -1.0843 -1.3125 1.6429 0.6833 JFK -0.1182 -Z4589 -3.1523 -0.0417 -1.0484 -1.1258 1.7128 0 .6 0 3 7 LGA -0.2912 -1.9713 -Z 4 2 3 5 -0.1949 -0.8145 -1 .7 5 8 5 1 .5 4 1 7 1.0321 M SP -0 .3 3 4 7 -Z0553 -Z6530 -0.2020 -0.9674 -1.8890 1.7680 1.0670 BOS -0.4348 -1.9442 -Z6852 -0.2 40 1 -0 .7 0 5 5 -Z0695 1.7428 0.9622 DEN -0 .4 1 1 4 .2 .6 2 2 2 -3 .6 1 9 0 -0.1702 -0.7511 -1.6849 Z2402 0 .9 2 7 0 PHX -0.5223 -3.2292 -4.4169 -0.1 61 1 -0 .7 2 1 8 -1.5744 Z8S63 0.8810 LAX -0 .7 6 7 1 -3 .5 7 3 8 -4 .8 4 7 8 -0.2580 -0.8491 -1.7885 3.2860 1.1050 SFO -0.6 11 1 -3 .8 8 1 5 -5 .1 4 2 4 -0.2610 -0.9406 -1.9691 3.6017 1.1590 SEA -0 .6 9 1 5 -4.2217 -5.4098 -0 .1 9 0 2 -0 .8 6 1 3 -1 .8 0 7 6 3 .8 0 1 9 1 .04 57 Overall -0.2650 -Z1134Î -Z7596 -0.1228 -0.86351 -1.6007 1.67761 0.7595 Table 6.7 Route Elasticities for Origin-Destinatlon Markets 184 than that of air fare, which indicates that passengers response to the change in air fare tends to be stronger than to the change in travel time. Travel time is less elastic comparatively. Taking a close look at the variation of elasticities of either travel time or air fare among three types of routes, we can find that the demand for stopped trips is more elastic: a percentage change in travel time or fare elicited a greater percentage change in demand on stopped routes. This implies that passengers preference for direct flights is relatively stable, a small adjustment in air fare will not be sufficient to modify this general preference. On the contrary since people basically don't regard stopped trips as their primary travel option and tend to be more open- minded to this alternative, the adjustment in air fare will be more likely to affect passengers' demand for stopped routes. In contrast to direct elasticities, the cross elasticities are positively signed which means that the increase in air fare or travel time along the direct route will have the potential to raise the probability of passengers choosing stopped routes. Again the difference can be noticed among 0-D markets with various nonstop distance ranges, with stopped routes in long-haul markets being more elastic in response to the air fare adjustment on direct routes. For a short travel distance, passengers basically prefer direct 185 flights, thus the fare incentive will not be strong enough to pull them away from flying direct routes. As the travel distance increases, passengers will be more willing to consider the options of flying indirectly if a good deal exists in air fare. Another measure examined in this section will be the marginal rate of substitution (MRS) between two variables in the passenger route model. Here, we focus our analysis on the substitution between cost factor and time factor in air travel, to get some sense about the value of different time factors, say travel time and transfer(waiting) time. The corresponding substitution rates for travel time and total transfer time are shown in Table 6.8 for each origin- destination market. The MRS's of travel time are consistent in their negative sign, although there is a big variation in values. The values of the substitution rates between cost and total transfer time are more complicated as expected according to the discussion in previous section. If these substitution rates are organized based on 0-D markets of different distances (Table 6.9), the patterns of the distribution of the substitution rates are less irregular and relatively easy to analyze. First, passengers' overall value of travel time is higher than that of waiting time, 3.45 versus 0.39. The value of waiting time reflects travelers' value of the inconvenience 186 Destination Travel Ti w TctTrens£erTi*e CVG -0.8031 -2.8079 TPA -4.0895 5.3913 PIT -3.4292 5.0834 lAD -5.3468 2.2577 DCA -2.0305 -1.5744 CLE -3.5107 -3.7979 BWI -2.3158 -2.0960 DTW -7.6064 9.7874 MIA -1.2793 -1.3194 ORD -1.1915 -1.2863 PHL -1.8539 0.9420 MCI -0.5488 -1.7228 DFW -1.7033 -5.1935 EWR -0.0331 -2.5353 JFK -0.2434 1.8921 LGA -1.4537 -2.1347 MSP -3.5602 1.1203 BOS -2.3885 -2.0771 DEN -21.5822 -7.4711 PHX -1.2493 -0.6148 LAX -4.1025 -0.0667 SFO -3.9040 -0.5879 SEA -5.0144 -0.0520 Table 6.8 Marginal Rates of Substitution twtween Cost and Time for 0 -0 Markets Gxovp of Routas TravelTime TotTransfarTima < 500 mi -1.3002 -0.5424 500-1000mi -1.3445 -0.7413 1000-1500mi -21.5822 7.4711 1500-2000mi -3.2410 -0-0325 > 2000mi -5.5640 -0.5623 Overall -3.44G2 -0.3664 Table 6.9 Marginal Rates of Substitution l>etwfeen Cost and Time for Grouped Routes 187 involved at connecting hub airports on stopped routes. Since passengers normally plan their trips in advance and choose those connecting flights with short time lag, they are not likely to place a particularly high value on waiting time. Comparatively once passengers decide their flights they'll have no control on the in-plane travel time, thus travel time reflects a high disutility to them. Second, while the values of total waiting time fluctuate irregularly, it seems that the value of travel time increases with increasing 0-D distance. It suggests that longer travel distance and the corresponding prolonged in-plane travel time incur a higher disutility for passengers, and benefits could be generated for passengers by somehow shortening the travel time. 6.4 Remarks Atlanta-based trips demonstrate a highly diversified and complicated route structure. There is a large number of intermediate stops on the way from an origin to the destination. It shows that passengers will not simply follow the most direct and shortest-dis tance path to their destinations as assumed by many studies on hub network design. Instead real world routes include a lot of detours and circuits, some of which could involve quite long journeys. Meanwhile, airlines tend to coordinate their flights over the 188 entire network with several large hubs being the key airports in their route layout. Cincinnati emerges as the primary connecting hub for stopped flights from Atlanta to destinations in the northeast, while Dallas, Denver, Salt Lake City, Chicago O'Hare and Phoenix are important on the two-stop routes to the destination in the west. This complicated route structure leads to a highly mixed results in the logit-based passenger route choice model calibration. People generally prefer low air fare, short travel time and direct routes with small number of stops en route. The increase in the values of these variables will negatively affect passengers' welfare and incur a high disutility. Comparatively the roles played by connecting hub airports and the transfer time at these airports are difficult to generalize, or at least not as straightforward as the roles of the above three variables. This complication is closely related to the mixed functions of hub airports and their implications for passengers traveling on stopped routes. This complexity can also be reflected in the difference between the first and second hub airports. Large hub airports, such as Cincinnati, Dallas, Chicago O'Hare, may mean more frequent flights to a larger number of destinations all over the country. Passengers may feel their transfer and waiting times at these hubs are worthwhile. However, large hubs sometimes 189 may simply mean congestion, more delays, long check in/out time and potentially misconnecting their preferred flights, thus passengers' welfare could also be negatively affected. Passengers prefer direct trips to their destinations, and this preference is rather stable over short travel distance. Their adjustment will be more likely to be induced by change in air fare rather than travel time. This suggests that general adjustments in air fare will not lead to significant difference in the share of direct passengers for short-haul air travels. Comparatively, the demand for stopped trips show a much higher elasticity in response to the change in air fare. So, a bigger room exists for airlines to compete in the markets of stopped trips. Our model estimates a higher value of travel time than that of transfer or waiting time. Since our data are for general passenger trips, some possible difference between so- called pleasure travelers and business travelers could not be revealed, but it is worth mentioning here. It is expected that the value of transfer or waiting time between departures for business travelers is significantly bigger than that for pleasure travelers. Business travelers generally do not plan their trip far in advsuice, thus there is a high disutility to them of adjusting departure times to the schedule and capacity constraints of the air carriers. This suggests that business 190 travelers can benefit largely from, the increase in the frequency of service. Hub-and-spoke routing somehow involves a trade-off between travel time and waiting time between departures: the decrease in time between departures comes at the expense of additional travel time. The relative value of this trade-off may again be different depending upon the purpose of travel. Business travelers can receive a greater benefit from the hub- and-spoke routings (compared with infrequent direct service) at major hub airports. Because the passenger route model controls for transfer time between departures, it captures a substantial portion of the effect of schedule delay on traveler behavior. It captures frequency-dependent delay, and because stochastic delay (the delay encountered if a seat is not available on the best scheduled flight) is a function of frequency as well as the load factor, it also captures part of the effect of stochastic delay. Although they are insensitive to the remaining component of stochastic delay, our findings did provide among others a general picture of and shed a light on time-related passenger behavior in the airline hub-and-spoke network. 191 CHAPTER 7 CONCLUSIONS AND REC0M4ENDATIONS In this chapter, a brief summary is provided of the main findings and discussions in previous chapters along with the main conclusions from these chapters. Also, further avenue of research will be pointed out auid recommendations are made concerning further empirical analysis. 7.1 Develo]pnent of Airline Hub-and-Spoke Network auid Its Passenger Implications During the decades following airline deregulation, airlines have had virtually unfettered freedom to deploy resources and develop markets. As a result the domestic airline industry has undergone significant structural and operational change. In pursuit of network economies, major carriers have converted from linear structures to national hub-and-spoke networks, causing greater carrier concentration at many airports. Using hub-and-spoke configurations has increased the number of domestic origin and destination (O&D) markets served by each major carrier as well as the frequency 192 of service. The development and expansion of the hub-auad-spoke network is a mixed blessing to domestic air passengers. The analysis of the hub-and-spoke service leads to the following observations. (1) The hub-and-spoke systems of today offer travelers convenient service to more destinations than did the linear system they replaced. (2) The hubbing process by its very nature requires a large volume of frequent service and this leads naturally to a relatively high degree of concentration. Moreover, once hubs are established, carriers have a strong incentive to attempt to increase their control of traffic at their connecting hubs. In recent years every major carrier expanded its existing hub-and-spoke operation by establishing new connecting complexes or intensifying existing connecting complexes at most large FAA hubs and several medium FAA hubs. The result has been a large increase in carrier concentration at such hubs. (3) Smaller spoke points also tend to receive services to more destinations via major connecting hubs. This service gives spoke communities the ability to travel to many large cities throughout the country. These observations suggest that air passengers could benefit from the airline hub-aind-spoke operation, in terms of the increased number of city pairs served, more frequent flights, more direct flights from hubs, more' opportunities for same day 193 return flights, and ease of online transfers. It has to be pointed out, however, that these passenger benefits are not evenly distributed over all the routes and over all the passengers. The routes with higher density of traffic generally obtain more benefits than the other less dense routes or spokes. Among passengers, travelers residing at a hub airport city can fully utilize the hub as an origin and return destination for their trips, thus become the major beneficiaries of hub privileges. This is especially true for those business travelers at the hub city who demand for higher service quality. Those transit passengers, forming a large portion of passengers at the hub, then can opt for alternative routings. The full range of hub benefits may not be passed on to them. The analysis also reveals the hub-aind-spoke network's many unfavorable impacts on passengers, the most significant of which include diseconomies of route distance, congestion and delay at hubs, and monopoly price at hubs dominated by a single large air carrier. A hubbing system simply does not work in absence of a high degree of concentration at the connecting points. Traffic control at such points is an essential ingredient for a successful hubbing system, and traffic control, by definition, implies an element of market power. Many major carriers have monopoly power at their 194 primary hub airports, which translates into escalating fares. The monopoly power manifests itself at the hub in the form of higher fares for those passengers departing from or arriving at the hub. Travelers living in hub cities, who at one time were basking in the benefits of more frequent non-stop service, now find themselves at the mercy of a megacarrier and have little choice but to pay whatever is charged. Transit passengers who do not begin or end their trips at the hub will be very likely to fly more miles under the hub-and-spoke configuration. Due to the greater circuity, they may end up having to pay more, too. The universal adoption of the hub- and-spoke route structure makes congestion and delays inevitable, especially at the largest hub airports. 7.2 Air Fare Variations in the Hub-and-Spolce Systm The post-deregulation air fare demonstrates a great volatility, although a mileage-dependent fare structure can still somehow be detected. Large fare disparities can be found between long-haul and short-haul trips, between direct flights and stopped flights in the markets of similar stage length, between flights from different airports, and also between flights departing from the same airport at various times. Higher average fares are paid by passengers traveling to and from large connecting hubs, especially where one airline 195 dominates most of the service. Passengers in monopoly markets generally pay more than those in competitive markets. Travelers to and from nonhub airports also pay relatively high air fares. Comparatively^ the medium hub airports demonstrate a fare advantage where passengers pay an average fare lower than the mean of all the categories on the national basis. Under current market condition, passengers traveling on short-haul trips are paying higher prices than those flying on long trips. Since hubs dominated by a single carrier tend to be relatively short-haul, air fares are generally higher for short trips from concentrated hub airports. For short trips, fares must often be competitive with the cost of driving. For long trips there are usually several airlines to choose from, even at cities where one airline offers most of the flights. Competition that relies on other connecting hubs of transit passengers could result in lower air fares for long trips. While flying the direct route is generally most convenient and economical, one-stopped trips show price advantages with the increase in origin-destination distance. In the markets with origin-destination distance over 1,000 or 1,500 miles, one-stop trips have an edge in fare over both nonstop and two-stop trips. While many large cities in the United States have several airport options, choosing an alternative port may mean a 196 significant saving in air fare. A separate study recorded a dramatic drop in the premium at Chicago in late 1980s, while the premium in many major cities remained same or even increased. Most of the drop was apparently the result of increased competition for local traffic by such low-cost carriers as Southwest at the city's other major airport — Midway. 7.3 Structure of Passengers Routes Our analysis shows a highly diversified and complicated passenger route structure in the airline hub and spoke network. The existence of a large number of routes and intermediate stops for an origin-destination market strongly suggests that air passengers will not simply follow the most direct and shortest path to their destinations. Instead, they could tolerate and accept detours and circuits on their way, some of which could mean a very long journey. One the one hand, this situation may indicate that passengers do not have other options but to fly stopped and detoured routes. On the other hand, it could imply that passengers' preference is highly varied regarding their choice of routes in the hub and spoke network. The availability of scheduled flights and passengers' choice both contribute to this very complex airline hub and spoke route structure. 197 Generally in the hub-and-spoke system, direct, nonstop routes firmly dominate the network, in terms of the number routes and the volume of passengers. This is especially true for short-haul markets, where the passenger volume accounts for more than ninety percent of the total. In response to the fare advantage of one-stop, long-haul trips, the percentage of passengers choosing one-stop routes raises gradually. While the overall percentage of passengers on one-stop routes is less than twenty percent, it could be as high as seventy percent from certain airports/cities. Normally these airports are not first-ranking hubs with medium level passenger traffic, a large portion of which will have to transfer at other major hubs. Overall, two-stop routes have a very tiny niche in the market. Although a large number of intermediate stops is involved on two-stop routes, several large hubs normally emerge as the key airports in an airline's route network. Atlanta-based route structure reveals the importance of Cincinnati on two-stop routes flying northeast, and Dallas Fort Worth, Denver, Salt lake City, Chicago O'Hare in stopped flights flying northwest and west. Such hub airports actually become the focuses of the whole route layout, which indicates that airlines coordinate their flights over the entire network in pursuit of economies of network structure. 198 Our logit-based passenger route model is calibrated at various levels of aggregation focusing on Atlanta-based routes. The complicated route structure and the existence of various types of airports lead to highly mixed results. Our findings show that among all the route and node variables considered, air fare, in-plane travel time, and number of stops on route are the most consistent and significant ones. They clearly demonstrate passengers' preference over routes of low fare, direct, and short travel time. The proportions of passengers choosing direct and stopped routes will be affected by the change of these variables in a consistent manner. Depending upon the nature of the transfer airports, the transfer or waiting time could either increase or decrease a passenger's utility. While a relatively longer waiting time between departures means the availability of a preferred flight to the passenger's destination, the elapse of time at a major large hub airport is quite worthwhile, thus positively affecting the passenger's choice of that specific route. However, if the prolonged waiting time is simply a result of a large hub airport's congestion and delay, the passenger's welfare will be negatively affected, thus the chance of a passenger choosing this route will be lowered. So, the effects of transfer time and the nodal activity level at hub airports are highly varied. 199 Our model estimates an overall small passenger elasticity in response to travel time and a higher fare elasticity, which are reasonable estimates. Demand for air travel is price- elastic, and the demand has a relatively inelastic response to changes in in-plane travel time. Changes in travel time provided by an airline are not likely to have a appreciad)le effect on its share of the travel market for a specific type of route. Also, air passengers' choice of one-stop and two- stop routes will be more likely to be altered by the change in air fare or even travel time. The choice of direct routes shows a higher level of stability in response to the change in either air fare or travel time. Since passengers in general have some controls over their travel plan in advance, their value of the inconvenience of waiting between departures will not be particularly high, compared with that of in-plane travel time. Our estimates of the marginal rates of substitution between cost and time verify this notion. Meanwhile, our estimates further disclose that passengers tend to place a higher value on the travel time when they fly on long-haul trips, while the value of waiting time is generally stable over the distance. 200 7.4 Further Work The airline hub-and-spoke operation is a fairly complicated phenomenon, and the emulation of passengers' route choice in this complicated network proves to be of equal complexity. This project tries to analyze some most important characteristics of the hub-and-spoke network and their implications for general air passengers, based on which an attempt is made to evaluate the relatively importance or significance of several route and nodal variables. Although it is far from complete and comprehensive, it does shed a light on the nature of the hub-and-spoke system and some internal factors. Further work could be done with the availability of more detailed information. Some recommendations are provided as follows. First, the airports used in this analysis are pretty much those large and medium hubs with small hubs and nonhub airports not included. It will be fruitful if those ports are incorporated into the analysis. We could then perform this study in more specific market manner. We can have a closer look at the activities at hubs of various levels, and routes could be examined by categories such as nonhub-nonhub, nonhub- small hub, nonhub-medium hub, nonhub-large hub, and so on. The difference among these categories will provide more insights. The lack of information on nonhub airports and non-hubbing 201 carriers/ to a large extent/ prevents us from offering a more comprehensive picture about the operations of the hub-and- spoke network. Second/ since major carriers are dominating the operation of the airline hub-and-spoke network/ the knowledge of air carriers at various hub airports and on their routes will be highly desirable. The case study of major carriers' hub concentration and dominance of routes will reveal the operation of the hub-and-spoke network at a fine scale and disclose more in-depth differences among markets. Comparisons can also be made in fare/ service quality among hubs with single carrier monopoly/ two major carriers, three carriers and so o n . Third/ the data set used in this analysis is consumer- oriented which is generally the purpose of this study. However, because data like load factor are not available at the detailed individual origin-destination route level we are not able to fully examine the effects of some factors like stochastic delay. Meanwhile, with the knowledge of passenger behavior as derived from our research, the addition of industry side data to the project could lead to some equilibrium study of the airline hub-and-spoke market. Differences exist in air travel behavior between business travelers and pleasure travelers. 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(1995), Hub Choice Equilibrium in the U.S. Ai met, presented at Regional Science Association International 42nd Annual North American Meeting, Cincinnati, Ohio. 210 APPENDIX A THIRTY ONE MAJOR PASSENGER AIRPORTS Airport Code Airport and/or City ATL Atlanta BOS Boston BWI Baltimore/Washington Airport OLE Cleveland CVG Cincinnati DAL Dallas Love Field (Close in downtown airfield only used by Southwest Airlines) DCA. Washington National Airport DEN Denver DFW Dallas/Fort Worth International Airport DTW Detroit EWR Newark, New Jersey (Usually considered part of New York City) HOU Houston Hobby (Close in downtown airport used primarily by business travellers) lAD Washington Dulles Airport lAH Houston Intercontinental Airport JFK John F. Kennedy Airport (New York) LAX Los Angeles LGA LaGuardia Airport (New York) MCI Kansas City, MO MDW Midway Airport (Chicago) MEM Memphis MIA Miami MSP Minneapolis/St. Paul Airport, MN MSY New Orleans ORD O'Hare Airport (Chicago) PHL Philadelphia PHX Phoenix PIT Pittsburgh, PA SEA Seattle/Tacoma Airport, Washington SFO San Francisco Airport STL St. Louis TPA Tampa, FL 211 A P P E N D IX B AIRPORT CODES AND CITIES EQBT ÇQPÇ CITY ABQ Albuquerque New Mexico ATL Atlanta Georgia AUS Austin Texas AVL Asheville North Carolina BDL Hartford Connecticut BHM Birmingham Alabama BNA Nashville-Davidson Tennessee BOS Boston Massachusetts BTR Baton Rouge Louisicuia BUF Buffalo New York BWI Baltimore Maryland CAE Columbia South Carolina CHA Chattanooga Tennessee CHO Charlottesville Virginia CHS Charleston South Carolina CLE Cleveland Ohio CLT Charlotte North Carolina CMH Columbus Ohio COS Colorado Springs Colorado CRW Charleston West Virginia CVG Cincinnati Ohio DAY Dayton Ohio DEN Denver Colorado DFW/DAL Dallas Texas DSM Des Moines Iowa DTW Detroit Michigan EW Evansville Indiana EWR Newark New Jersey FLL Fort Lauderdale Florida FWA Fort Wayne Indiana GSO Greensboro North Carolina GSP Greenville South Carolina HSV Huntsville Alabama lAD/DCA Washington District of Columbia lAH/HOU Houston Texas IND Indianapolis Indiana JAN Jackson Mississippi JAX Jacksonville Florida JFK/LGA New York New York 212 LAS Las Vegas Nevada LAX Los Angeles California LEX Lexington-Fayette Kentucky LIT Little Rock Arkansas MCI Kansas City Missouri MCO Orlando Florida MEM Memphis Tennessee MIA Miami Florida MKE Milwaukee Wisconsin MOB Mobile Alabama MRY Monterey California MSN Madison Wisconsin MSP Minneapolis Minnesota MSY New Orleans Louisiana DKG Oklahoma City Oklahoma OMA Omaha Nebraska ■ ONT Ontario Ccilifomia ORD/MDW Chicago Illinois ORE Norfolk Virginia PBI West Palm Beach Florida PDX Portland Oregon PHF Newport News Virginia PHL Philadelphia Penns ylvania PHX Phoenix Arizona PIT Pittsburgh Penns ylvani a PNS Pensacola Florida PSP Palm Springs California PVD Providence Rhode Island RDU Raleigh North Carolina RIG Richmond Virginia RNO Reno Nevada RCA Roanoke Virginia ROG Rochester New York RSW Fort Myers Florida SAN San Diego California SAT San Antonio Texas SAV Savannah Georgia SBY Salisbury Maryland SGE State College Penns ylvani a SDF Louisville Kentucky SEA Seattle Washington SFO San Francisco California SHV Shreveport Louisiana SLG Salt Lake City Utah SNA Orange California STL St. Louis Missouri TLH Tallahassee Florida TPA Tampa Florida TRI Kingsport Tennessee TUL Tulsa Oklahoma TUS Tucson Arizona TYS Mioxville Tennessee 213