Commuting Include the New UW Research Park on Into Dane County Represented About a 33% Madison’S West Side, the Greentech Village Increase Compared to 2000

Total Page:16

File Type:pdf, Size:1020Kb

Commuting Include the New UW Research Park on Into Dane County Represented About a 33% Madison’S West Side, the Greentech Village Increase Compared to 2000 transit, particularly if developed with higher Around 40,000 workers commute into densities, mixed-uses, and pedestrian- Dane County from seven adjacent friendly designs. While most of the existing counties, according to 2006-2008 American peripheral centers have not been designed Community Survey (ACS) data. Of these, in this fashion, plans for many of the new about 22,600 commuted to the city of centers are more transit-supportive. These Madison. The number of workers commuting include the new UW Research Park on into Dane County represented about a 33% Madison’s west side, the GreenTech Village increase compared to 2000. While the 2006- and McGaw Neighborhood areas in the City 2008 and 2000 data sources aren’t directly of Fitchburg, and the Westside Neighborhood comparable, the numbers are consistent on the City of Sun Prairie’s southwest side. with past trends from decennial Census data. “Reverse commuting” from Dane Figure 6 shows the existing and planned County to adjacent counties did not show future major employment/activity centers the same increase. Around 8,400 commuted along with their projected 2035 employment. to adjacent counties, about the same as in The map illustrates the natural east-west 2000. Figure 7 shows work trip commuting to corridor that exists, within which future high- and from Dane County. capacity rapid transit service could connect many of these centers. Around 62,600 workers commuted to the city of Madison from other Dane County Commuting – Where We Work and communities in 2009, according to U.S. Census Longitudinal Employer-Household How We Get There Dynamics (LEHD) program data. This number is consistent with and somewhat higher than The growth of major employment and the 60,000 in 2000 reported from decennial commercial retail centers on the periphery Census data. The increase in commuting into of the Madison area has resulted in a more Madison continues past trends. However, dispersed work travel pattern. While the the rate of increase has slowed since 1990 downtown Madison/UW-Madison campus as other communities have attracted more area remains by far the largest employment employment. Figure 8 shows the percentage center, there has been very little net increase of workers in Dane County communities in employment in the area over the past two commuting to the city of Madison. decades. As a result, a lower percentage of trips are destined for the central Madison area. As previously mentioned, growth in Dane County and city of Madison employment has outpaced the growth in the resident labor force and this trend is expected to continue. The same trend has occurred in the cities of Middleton and Monona, though the numbers are much smaller. The labor force from surrounding counties and communities fills the gap in workers by commuting into the county and Madison area on a daily basis. 22 2035 Regional Transportation Plan Update March 2012 2035 Regional Transportation PlanUpdate Transportation 2035 Regional Figure 6 Madison Area TT PP BB !P Transportation Planning Board !P !P A Metropolitan Planning Organization (MPO) Æÿ T o w n o f R o x b u r y Æÿ Æÿ Æÿ Æÿ !P !P Æÿ T o w n o f B r i s t o l T o w n o f Y o r k ÆÿV ÆÿV York Center 0 2 4 T o w n o f D a n e Village of Dane !P Major T o w n o f W i n d s o r ÆÿN ÆÿKP (/51 ÆÿI Miles ÆÿCV Employment / Activity (/12 Windsor (/151 ÆÿC 4,000 ÆÿP !P Centers 5,400 Æ· 113 Æ· 90 TT ¨¦§ ÆÿN Æÿ 94 ( ¨¦§ City of Sun Prairie ÆÿKP ¨¦§39 Major Employment / Activity Center Existing Limited Service Area 7,000 Token Creek Marxville Springfield Corners T o w n o f Æ·19 !P !P !P Æ·19 Environmental Corridor Existing Urban Service Area 19 Æ·19 Æ· Village of ( W e s t p o r t Æ·19 Village of Marshall Waunakee Deansville Isolated Resource Feature MPO Boundary Martinsville Æ·113 !P !P Æ·19 ÆÿKP 4,500 (/151 ( T o w n ÆÿC Æ·19 6,200 ÆÿN 1,200 o f ÆÿK B u r k e ÆÿP 17,800 Projected 2035 Employment (51 T ÆÿCV / ( Æÿ ( T o w n o f B e r r y T o w n o f S p r i n g f i e l d ÆÿQ ( T o w n o f S u n P r a i r i e (/151 ÆÿT ÆÿM 90 ÆÿK ÆÿK ¨¦§ Æ·113 ¨¦§94 Ashton T o w n o f M e d i n a Dane County ¨¦§39 Æ·73 ÆÿK !P 7,400 Burke ÿP 0 5 10 Æ ÆÿCV 6,300 !P ÆÿTT 2,900 ( ÆÿT Miles ÆÿM ÆÿQ 51 ÆÿKP (/ (/151 ÆÿN ÆÿKP (113 2,800 4,30Æ·0 (/12 (( 7,100 Village of 5,400 ÆÿTT Village of Cross Plains City of Village of ¨¦§94 DeForest T 30 Æÿ Village of Dane M Maple Bluff Æ· Middle(ton Æÿ Lake Mendota (/14 ( ¨¦§94 11,300 Q 32,500 Æÿ City of Sun Prairie 23 (/14 151 (/ Village of Village of Marshall Village of Waunakee ( Mazomanie ( 20,300 City of Madison ÆÿP Village of 8,500 ÆÿBB 8,200 Village of Shorewood 90 T o w n o f D e e r f i e l d ¨¦§ T o w n o f D e eBBr f i e l d Æÿ Village of Cottage Grove Black Earth MS Hills Æÿ ¨¦§39 Village of Cross Plains City of Village of 51 Maple Bluff (/ Middleton City of City of Madison Village of BB Village of T o w n o f Æÿ Shorewood T o w n o ff Hills Cottage Grove Monona City of T o w n o f C r o s s P l a i n s ( Monona T o w n o f C r o s s P l a i n s 1,800 12,700 13,800 B l o o m i n g M ii d d ll e t o n 6,800 9,600 B l o o m i n g 73 Village of Deerfield 6,400 Æ· City of Madison Lake Monona G rr o v e ( BB ÆÿAB 7,500 ( Æÿ Æ· Pine Bluff Village of Village of McFarland Village of Deerfield Mount Horeb ( City of !P S Verona Æÿ S Village of Village of Æÿ Cambridge Blue Mounds City of Madison ( (/51 T o w n o f C o t t a g e G r o v e City of Fitchburg 5,200 (/12 Hope (/14 Lake Wingra N ( Æÿ Village of 3,300 !P Rockdale ÆÿP ( ( ÆÿMC !P T o w n o ff ÆÿBW ((/12 (/18 ( City of 6,400 Stoughton ( Village of M a d i s o n ( M a d i s o n Oregon ÆÿM ( ( 12 (/14 (/18 (151 (/ / (/51 90 Village of ¨¦§ Nora Belleville 39 Village of ¨¦§ !P Brooklyn (12 (/18 City of Edgerton ÆÿS ÆÿAB / Riley (/151 ( Klevenville !P ( !P (/18 Village of ÆÿN 4,400 McFarland ÆÿPD ÆÿPD ÆÿMM ÆÿMN Lake Waubesa City of ÆÿW ÆÿP ÆÿPD 3,700 Isthmus ÆÿID Verona (14 / Æ·73 Village of ÆÿM D ÆÿAB Cambrid0ge 2,000 4,000 (/151 6,000 Æÿ (/ (/151 ( Mud T o w n o f S p r i n g d a l e Lake Feet City( of Fitchburg Æ· ÆÿMV T o w n o f ¨¦§90 ÆÿPB Lake Mendota ( SunnysPidel e a s Kaegnont s aS p r i n g s ¨¦§39 !P !P T o w n o f C h r i s t i a n a Æ·69 ÆÿM (/51 ÆÿG (/18 151 32,500 (/ ÆÿW ÆÿB Utica 8,500 !P ÆÿB 20,300 Lake Kegonsa Æÿ T o w n o f D u n n ÆÿB ÆÿB T o w n o f Æ· 69 V e r o n a ÆÿG ÆÿMM ÆÿN Mt. Vernon ÆÿM ÆÿM !P ÆÿPB ( ÆÿW ÆÿB ( Æÿ ÿMM D Æ Lake Monona Æÿ ÆÿA Paoli (/14 CC N ( !P Æÿ Æÿ ( (/51 City of 6,400 Stoughton ÆÿG Village of Æ·138 (/51 T o w n o f M o n t r o s e Oregon !P Basco ÆÿD !P ÆÿA ÆÿA ÆÿPB Æ·138 ÆÿMM T o w n o f P r i m r o s e ÆÿA A ( ÆÿA A Æÿ Æÿ Æÿ ÆÿA T o w n o f R u t l a n d ÆÿA March 2012 March !P Æ·92 ÆÿA T o w n o f D u n k i r k T o w n o f A l Æ·b73 i o n A ÆÿA N Æÿ Æÿ ÆÿA Source Info: T o w n o f O r e g o n Activity Centers: 3/11 (MATPB). Æÿ Montrose Open Space: 6/06 (CARPC). !P Albion Service Areas: 3/11 (CARPC). PB Æÿ !P Street Base: 4/10, Orthophoto Derived (MAMPO/DCLIO). Æ·69 Village of Functional Classifications: 4/10 (MATPB, WDOT). ÆÿG ÆÿD Hydrography: 12/00, 1:24,000 (WIDNR). © Belleville Civil Division Limits: 3/11, Annexation Records (MATPB/DCLIO). ÆÿD MPO Boundary: 9/02 (MATPB. Date: 1/25/2012 ÆÿN ( Author: pldms Village of Date Saved: 12/28/2011 4:27:00 PM Lambert Conformal Conic Projection Brooklyn Æÿ Path: U:\ArcMapPrj\Employment\DaneCounty\RTP\ActivityCenters_PageSize_2035.mxd WISCRS- Dane, NAD 83(91) Æ· Æÿ Æÿ Æ· Æÿ Æ· Æ· ( 2035 Regional Transportation PlanUpdate Transportation 2035 Regional Figure 7 Green Juneau Adams Marquette Fond Du Lake Lac Wisconsin Vernon Sheboygan Columbia ´ Dodge Sauk Washington Richland 0 1 5 0 2 4 5 2 0 4 6 3 0 7 0 3 6 2 1 4 9 0 0 1 1495 790 24 Dane 3800 5115 Jefferson Waukesha Iowa 1 0 9 Grant 5 8 9 4 3 7 0 5 4 1 5 5 Racine Rock Walworth Lafayette Green (No Data Available *) Kenosha Total Trips to Dane County: 40,335 Total Trips from Dane County: 8,400 March 2012 March * CTTP product limited to geographies with populations 20,000 or greater.
Recommended publications
  • Park-And-Ride Study: Inventory, Use, and Need
    Park-and-Ride Study: Inventory, Use, and Need For the Roanoke and New River Valley regions Contents Background ..................................................................................................................................... 1 Study Area ................................................................................................................................... 1 Purpose ....................................................................................................................................... 2 Methodology ............................................................................................................................... 3 Existing Facilities ............................................................................................................................. 4 Performance Measures ................................................................................................................... 9 Connectivity ................................................................................................................................ 9 Capacity ....................................................................................................................................... 9 Access ........................................................................................................................................ 12 General Conditions ................................................................................................................... 13 Education .....................................................................................................................................
    [Show full text]
  • Travel Characteristics of Transit-Oriented Development in California
    Travel Characteristics of Transit-Oriented Development in California Hollie M. Lund, Ph.D. Assistant Professor of Urban and Regional Planning California State Polytechnic University, Pomona Robert Cervero, Ph.D. Professor of City and Regional Planning University of California at Berkeley Richard W. Willson, Ph.D., AICP Professor of Urban and Regional Planning California State Polytechnic University, Pomona Final Report January 2004 Funded by Caltrans Transportation Grant—“Statewide Planning Studies”—FTA Section 5313 (b) Travel Characteristics of TOD in California Acknowledgements This study was a collaborative effort by a team of researchers, practitioners and graduate students. We would like to thank all members involved for their efforts and suggestions. Project Team Members: Hollie M. Lund, Principle Investigator (California State Polytechnic University, Pomona) Robert Cervero, Research Collaborator (University of California at Berkeley) Richard W. Willson, Research Collaborator (California State Polytechnic University, Pomona) Marian Lee-Skowronek, Project Manager (San Francisco Bay Area Rapid Transit) Anthony Foster, Research Associate David Levitan, Research Associate Sally Librera, Research Associate Jody Littlehales, Research Associate Technical Advisory Committee Members: Emmanuel Mekwunye, State of California Department of Transportation, District 4 Val Menotti, San Francisco Bay Area Rapid Transit, Planning Department Jeff Ordway, San Francisco Bay Area Rapid Transit, Real Estate Department Chuck Purvis, Metropolitan Transportation Commission Doug Sibley, State of California Department of Transportation, District 4 Research Firms: Corey, Canapary & Galanis, San Francisco, California MARI Hispanic Field Services, Santa Ana, California Taylor Research, San Diego, California i Travel Characteristics of TOD in California ii Travel Characteristics of TOD in California Executive Summary Rapid growth in the urbanized areas of California presents many transportation and land use challenges for local and regional policy makers.
    [Show full text]
  • Transit-Oriented Development and Joint Development in the United States: a Literature Review
    Transit Cooperative Research Program Sponsored by the Federal Transit Administration RESEARCH RESULTS DIGEST October 2002—Number 52 Subject Area: VI Public Transit Responsible Senior Program Officer: Gwen Chisholm Transit-Oriented Development and Joint Development in the United States: A Literature Review This digest summarizes the literature review of TCRP Project H-27, “Transit-Oriented Development: State of the Practice and Future Benefits.” This digest provides definitions of transit-oriented development (TOD) and transit joint development (TJD), describes the institutional issues related to TOD and TJD, and provides examples of the impacts and benefits of TOD and TJD. References and an annotated bibliography are included. This digest was written by Robert Cervero, Christopher Ferrell, and Steven Murphy, from the Institute of Urban and Regional Development, University of California, Berkeley. CONTENTS IV.2 Supportive Public Policies: Finance and Tax Policies, 46 I INTRODUCTION, 2 IV.3 Supportive Public Policies: Land-Based I.1 Defining Transit-Oriented Development, 5 Initiatives, 54 I.2 Defining Transit Joint Development, 7 IV.4 Supportive Public Policies: Zoning and I.3 Literature Review, 9 Regulations, 57 IV.5 Supportive Public Policies: Complementary II INSTITUTIONAL ISSUES, 10 Infrastructure, 61 II.1 The Need for Collaboration, 10 IV.6 Supportive Public Policies: Procedural and II.2 Collaboration and Partnerships, 12 Programmatic Approaches, 61 II.3 Community Outreach, 12 IV.7 Use of Value Capture, 66 II.4 Government Roles, 14
    [Show full text]
  • What Affects Commute Mode Choice: Neighborhood Physical Structure Or Preferences Toward Neighborhoods?
    Journal of Transport Geography 13 (2005) 83–99 www.elsevier.com/locate/jtrangeo What affects commute mode choice: neighborhood physical structure or preferences toward neighborhoods? Tim Schwanen a,*, Patricia L. Mokhtarian b a Urban and Regional Research Center Utrecht (URU), Faculty of Geosciences, Utrecht University, P.O. Box 80.115, 3508TC Utrecht, The Netherlands b Department of Civil and Environmental Engineering and Institute of Transportation Studies, One Shields Avenue, University of California, Davis, Davis CA 95616, USA Abstract The academic literature on the impact of urban form on travel behavior has increasingly recognized that residential location choice and travel choices may be interconnected. We contribute to the understanding of this interrelation by studying to what extent commute mode choice differs by residential neighborhood and by neighborhood type dissonance—the mismatch between a com- muterÕs current neighborhood type and her preferences regarding physical attributes of the residential neighborhood. Using data from the San Francisco Bay Area, we find that neighborhood type dissonance is statistically significantly associated with commute mode choice: dissonant urban residents are more likely to commute by private vehicle than consonant urbanites but not quite as likely as true suburbanites. However, differences between neighborhoods tend to be larger than between consonant and dissonant residents within a neighborhood. Physical neighborhood structure thus appears to have an autonomous impact on commute mode choice. The analysis also shows that the impact of neighborhood type dissonance interacts with that of commutersÕ beliefs about automobile use, suggesting that these are to be reckoned with when studying the joint choices of residential location and commute mode.
    [Show full text]
  • Office Development, Rail Transit, and Commuting Choices
    Office Development, Rail Transit, and Commuting Choices Office Development, Rail Transit, and Commuting Choices Robert Cervero, University of California, Berkeley Abstract Decentralized employment growth has cut into transit ridership across the United States. In California, about 20 percent of those working in office buildings near rail stations regularly commute by transit, nearly three times transit’s modal share among those working away from rail stations. Mode choice models reveal that office workers are most likely to rail-commute if frequent feeder bus services are available, their employers help cover the cost of taking transit, and parking is in short sup- ply. Factors like trip-chaining and the absence of restaurants and retail shops near suburban offices, however, deter transit-commuting. Policy-makers can promote transit-commuting to offices near rail stops by flexing parking standards, introducing high-quality feeder buses, and initiating workplace incentives such as deeply dis- counted transit passes. While housing has generally been the focus of transit-oriented development, unless the other end of the commute trip—the workplace—is also convenient to transit, transit will continue to struggle in winning over commuters in an environment of increasingly decentralized employment growth. Introduction Transit oriented development (TOD)—compact, mixed-use development around transit stations—has gained popularity as a smart-growth strategy. A national survey recently identified more than 00 TODs across the United States that were self-identified by local transit-agency planners (Cervero et al. 004). TOD is arguably the most cogent form of smart growth: lay citizens and politicians alike 4 Journal of Public Transportation, Vol.
    [Show full text]
  • Spatial Patterns
    SPATIAL PATTERNS In keeping with long-term trends, people and jobs METROPOLITAN SPRAWL Households have been steadily migrating from densely settled are moving away from high-density center cities urban cores to lower-density areas for decades, encouraged in large part by the expansion of the highway system and to lower-density suburbs and outlying areas. the ideal of single-family suburban living. During the 1970s, 84 high-density center cities (with 1970 populations of over But not all households are benefiting from the 100,000) experienced significant population losses—a collective total of 4.2 million residents or 11.3 percent of their outward push of development. Many low-income 1970 populations. families can neither afford the higher rents nor Although population in most of these areas then stabilized, 32 cities sustained ongoing losses in the 1980s and 1990s. By take advantage of the employment opportunities 2000, this group had lost 27 percent of their 1970 population base. Among the most spectacular losers were Detroit in these far-flung communities. (down 563,000), Philadelphia (down 431,000), St. Louis (down 314,000) and Baltimore and Cleveland (each down just over 250,000). FIGURE 14 The Pace of Sprawl Varies by Race as Well as Tenure Median Distance from CBD (Miles) Owners All 9.8 11.9 13.0 13.8 White 10.1 12.7 13.8 14.7 Hispanic 7.5 9.4 10.2 11.0 Black 5.4 7.2 8.1 9.0 Renters All 7.4 8.3 9.1 9.4 White 7.7 8.5 10.1 10.6 Hispanic 7.4 7.6 8.6 8.9 Black 4.3 5.7 6.8 7.4 3 4 5 6 7 8 9 10 11 12 13 14 15 ■ 1970 ■ 1980 ■ 1990 ■ 2000 Source: Table A-3.
    [Show full text]
  • Measuring Commuting and Economic Activity Inside Cities with Cell Phone Recordsthe Authors Are Grateful to the Lirneasia Organiz
    Measuring Commuting and Economic Activity inside Cities with Cell Phone Records∗ Gabriel E. Kreindler† Yuhei Miyauchi‡ February 21, 2019 JEL Codes: C55, E24, R14 Abstract We show that commuting flows constructed from cell phone transaction data predict the spatial distribution of wages and income in cities. In a simple workplace choice model, commuting flows follow a gravity equation whose destination fixed effects correspond to wages. We use cell phone data from Dhaka and Colombo, covering hundreds of millions of commuter-day observations, to invert this relationship. Model-predicted income at the workplace level predicts self-reported survey workplace income, and model-predicted residential income predicts nighttime lights. In an application, we estimate that predicted commuter income is 4-5% lower on days with hartals (transportation strikes) in Dhaka. ∗The authors are grateful to the LIRNEasia organization for providing access to Sri Lanka cell phone data, and especially to Sriganesh Lokanathan, Senior Research Manager at LIRNEasia. The authors area also grateful to Ryosuke Shibasaki for navigating us through the cell phone data in Bangladesh, to Anisur Rahman and Takashi Hiramatsu for the access to the DHUTS survey data, and International Growth Center (IGC) Bangladesh for hartals data. The cell phone data for Bangladesh is prepared by the Asian Development Bank for the project (A-8074REG: “Applying Remote Sensing Technology in River Basin Management”), a joint initiative between ADB and the University of Tokyo. We are grateful to Akira
    [Show full text]
  • Analysis of Bicycle Commuting in American Cities
    Analysis of bicycle commuting in American cities RIDE REPORT ON 2016 AMERICAN COMMUNITY SURVEY DATA BY THE LEAGUE OF AMERICAN BICYCLISTS LEAGUE OF AMERICAN BICYCLISTS 2016 AMERICAN COMMUNITY SURVEY DATA REPORT WHERE WE From 2000 to 2016, bicycle RIDING TO WORK BY THE NUMBERS commuting has seen Where is bike 51% commuting growing in GROWTH NATIONWIDE the United States? Nationwide, in 2016, there were a total of Every year, the U.S. Census Bureau studies Americans’ commuting habits, including how many people commute by bike. While commuting is only part of the bicycling story, the American Community Survey 863,979 provides valuable insight into changing commuting patterns and transportation choices. BIKE COMMUTERS The city with the highest % of Each year, the League of American Bicyclists digs into residents biking to work: the data to assess the state of bicycle commuting in cities across the country — and gives you a glimpse into how your community stacks up. 16.6% Here’s our analysis of the 2016 numbers. DAVIS, CALIFORNIA 2 WHERE WE RIDE: ANALYSIS OF BICYCLE COMMUTING IN AMERICAN CITIES CITIES WITH THE MOST BICYCLISTS IN 2016 These cities have the largest number of bicyclists riding on their streets. % OF BIKE CITY STATE POPULATION BICYCLISTS COMMUTERS NEW YORK NEW YORK 8,537,673 48,601 1.2% CHICAGO ILLINOIS 2,704,965 22,449 1.7% PORTLAND OREGON 639,635 21,982 6.3% LOS ANGELES CALIFORNIA 3,976,324 20,495 1.1% SAN FRANCISCO CALIFORNIA 870,887 19,429 3.9% WASHINGTON DISTRICT OF COLUMBIA 681,170 16,647 4.6% SEATTLE WASHINGTON 704,358 14,801
    [Show full text]
  • Determinants of Bicycle Commuting in the Washington, DC Region: the Role of Bicycle Parking, Cyclist Showers, and Free Car Parking at Work
    Transportation Research Part D 17 (2012) 525–531 Contents lists available at SciVerse ScienceDirect Transportation Research Part D journal homepage: www.elsevier.com/locate/trd Determinants of bicycle commuting in the Washington, DC region: The role of bicycle parking, cyclist showers, and free car parking at work Ralph Buehler Urban Affairs and Planning, Virginia Tech, Alexandria Center, 1021 Prince Street, Room 228, Alexandria, VA 22314, USA article info abstract Keywords: This article examines the role of bicycle parking, cyclist showers, free car parking and tran- Bicycling to work sit benefits as determinants of cycling to work. The analysis is based on commute data of Bicycle parking workers in the Washington, DC area. Results of rare events logistic regressions indicate that Car parking bicycle parking and cyclist showers are related to higher levels of bicycle commuting—even Cyclist showers when controlling for other explanatory variables. The odds for cycling to work are greater Trip-end facilities for employees with access to both cyclist showers and bike parking at work compared to those with just bike parking, but no showers at work. Free car parking at work is associated with 70% smaller odds for bike commuting. Employer provided transit commuter benefits appear to be unrelated to bike commuting. Regression coefficients for control variables have expected signs, but not all are statistically significant. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Over the last decades US cities have increasingly promoted bicycle commuting to reduce local and global air pollution, combat peak hour traffic congestion, and achieve health benefits from physical activity (Alliance for Biking and Walking, 2012).
    [Show full text]
  • 2020 Regional Mode Share Report
    ACTIVE TRANSPORTATION ALLIANCE 2020 Regional Mode Share Report Released February 2020 and updated with 2018 data activetrans.org 1 The mission of the Active Transportation Alliance is to advocate for walking, bicycling, and public transit to create healthy, sustainable, and equitable communities. Our goal for 2025 is to see 50 percent of all trips in the region made by people walking, biking, or using public transit. The following report tracks mode share trends for people commuting by car, transit, foot, and bike since 1980. A 2018 regional snapshot with the most recent available data is below. 2018 Chicagoland mode share snapshot1 77.4% 13.2% 5.6% 2.9% 0.7% The automobile remains the dominate commute Public transportation Working from Walking is a Biking is slowly mode of choice in the region. However, not alleviates traffic home is becoming healthy, low-impact becoming a more everyone owns a car. Two percent of suburban congestion and reduces increasingly commute option popular commute workers and 16 percent of City of Chicago greenhouse gas emissions. popular, growing that is unfortunately option in Chicago, workers lack access to a car. One CTA bus can take as at a faster rate becoming a less yet remains only many as 60 cars off than any common way of a small segment the road.2 other mode. getting to work. of our overall regional mode share. Sources: American Community Survey and Chicago Transit Authority NOTE: In this report, Chicagoland or the region refers to Cook County, DuPage County, Lake County, Kane County, Kendall County, McHenry County and Will County unless otherwise noted in the footnotes.
    [Show full text]
  • Innovative Suburb-To-Suburb Transit Practices
    T R A N S I T C O O P E R A T I V E R E S E A R C H P R O G R A M SPONSORED BY The Federal Transit Administration TCRP Synthesis 14 Innovative Suburb-to-Suburb Transit Practices A Synthesis of Transit Practice Transportation Research Board National Research Council TCRP OVERSIGHT AND PROJECT TRANSPORTATION RESEARCH BOARD EXECUTIVE COMMITTEE 1995 SELECTION COMMITTEE CHAIRMAN OFFICERS ROD J. DIRIDON International Institute for Surface Chair: LILLIAN C. BORRONE, Director, Port Department, The Port Authority of New York and New Jersey Transportation Policy Study Vice Chair: JAMES W. VAN LOBEN SELS, Director, California Department of Transportation Executive Director: ROBERT E. SKINNER, JR, Transportation Research Board, National Research Council MEMBERS SHARON D. BANKS AC Transit MEMBERS LEE BARNES Barwood, Inc EDWARD H. ARNOLD, Chairman & President, Arnold Industries, Inc GERALD L. BLAIR SHARON D. BANKS, General Manager, Alameda-Contra Costa Transit District, Oakland, California Indiana County Transit Authority BRIAN J. L. BERRY, Lloyd Viel Berkner Regental Professor & Chair, Burton Center for Development Studies, MICHAEL BOLTON University of Texas at Dallas Capital Metro DWIGHT M. BOWER, Director, Idaho Department of Transportation SHIRLEY A. DELIBERO JOHN E. BREEN, The Nasser I Al-Rashid Chair in Civil Engineering, The University of Texas at Austin New Jersey Transit Corporation WILLIAM F. BUNDY, Director, Rhode Island Department of Transportation SANDRA DRAGGOO DAVID BURWELL, President, Rails-to-Trails Conservancy CATA A. RAY CHAMBERLAIN, Vice President, Freight Policy, American TruckingAssociations, Inc LOUIS J. GAMBACCINI (Past Chair 1993) SEPTA RAY W. CLOUGH, Nishkian Professor of Structural Engineering, Emeritus, University of California, Berkeley DELON HAMPTON JAMES C.
    [Show full text]
  • Transportation Land Use Study
    www.vtpi.org [email protected] 250-508-5150 Transportation Land Valuation Evaluating Policies and Practices that Affect the Amount of Land Devoted to Transportation Facilities 24 March 2020 Todd Litman Victoria Transport Policy Institute A relatively large portion of urban land is devoted to roads and parking facilities. Abstract This paper explores the relationships between transportation, land use and taxation. It investigates how current land tax and regulatory practices affect the amount of land devoted to roads and parking facilities, and how this affects transport patterns. It discusses ways to measure the amount of land devoted to transport facilities, examine how this varies under different circumstances, estimate the value of this resource, evaluate how tax policies and regulations policies treat this land, and analyze whether current practices are optimal in terms of various economic and social objectives. Todd Alexander Litman © 1995-2020 You are welcome and encouraged to copy, distribute, share and excerpt this document and its ideas, provided the author is given attribution. Please send your corrections, comments and suggestions for improvement. Transportation Land Valuation Victoria Transport Policy Institute Introduction This report summarizes research exploring the relationships between transportation, land use and taxation. It investigates whether current transportation and land use practices result in an economically optimal amount of land being devoted to transportation facilities, particularly roads and parking. Roads are essential for most forms of mobility, and parking is essential for automobility (travel by automobile). However, just because these facilities provide benefits does not mean that current policies are optimal. Just as doctors must be careful to avoid prescribing too much medicine, transport policies must prescribe just the right amount of road and parking capacity in each situation.
    [Show full text]