DOCSLIB.ORG

Refinement of Fsutms Trip Distribution Methodology

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Refinement of Fsutms Trip Distribution Methodology REFINEMENT OF FSUTMS TRIP DISTRIBUTION METHODOLOGY 10.00 9.00 Final Report Survey 8.00 Contract No. BB942 Destination Choice Model with Spatial Variables 7.00 Destination Choice Model without Spatial Variable 6.00 "Gravity Model" Prepared for 5.00 4.00 Percentage of Trips (%) 3.00 Research Office 2.00 Florida Department of Transportation 1.00 0.00 0 5 10 15 20 25 30 35 40 45 50 55 60 Prepared by Lehman Center for Transportation Research Department of Civil & Environmental Engineering Florida International University September 2004 REFINEMENT OF FSUTMS TRIP DISTRIBUTION METHODOLOGY Final Report Contract No. BB942 Prepared for Research Office Florida Department of Transportation 605 Suwannee Street, MS 30 Tallahassee, FL 32399-0450 Prepared by Fang Zhao, Ph.D., P.E. Associate Professor Lee-Fang Chow, Ph.D. Senior Research Associate Min-Tang Li, Ph.D. Senior Research Associate and Albert Gan, Ph.D. Associate Professor Lehman Center for Transportation Research Department of Civil & Environmental Engineering Florida International University University Park Campus, EAS 3673 Miami, Florida 33199 Phone: 305-348-3821 Fax: 305-348-2802 E-mail: [email protected] September 2004 Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. Final Report for BB942 4. Title and Subtitle 5. Report Date September 2004 REFINEMENT OF FSUTMS TRIP DISTRIBUTION METHODOLOGY 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Fang Zhao, Lee-Fang Chow, Min-Tang Li, Albert Gan 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) Lehman Center for Transportation Research, Department of Civil and Environmental Engineering, Florida International University, 11. Contract or Grant No. Miami, Florida 33199 BB942 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered Research Office Final Report Florida Department of Transportation December 1998 – September 2004 650 Suwannee Street, MS 30 Tallahassee, Florida 32399-0450 14. Sponsoring Agency Code 15. Supplementary Notes 16. Abstract In this report, alternative trip distribution models are investigated with the purpose of improving the current trip distribution methodology in FSUTMS. Three types of models are studied: intervening opportunity models, enhanced gravity models, and destination choice models. The performance of the models was compared against travel survey data and that of traditional gravity models. The evaluation criteria included average trip length, trip length distribution, intrazonal trips, and spatial accuracy measured by different statistical tests. The findings from the project indicate that intervening opportunity models and enhanced gravity models did not produce noteworthy improvements, although the intervening opportunity models were handicapped by the lack of suitable software for model calibration therefore no definite conclusions may be drawn regarding their potentials. Aggregate destination choice models were developed for Broward, Palm Beach, and Volusia counties for Home- Based Work (HBW) trip purpose. Although the three counties are different in their demographics, socioeconomics, and urban structures, improvements are seen in the trip distribution results produced by all the destination choice models, although the improvements for Broward and Volusia counties are more significant than those for Palm Beach County. Results from destination choice models for other trips purposes also indicate improvements. 17. Key Word 18. Distribution Statement Trip distribution, gravity model, intervening opportunity model, destination choice model 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price Unclassified. Unclassified. 165 Form DOT F 1700.7 (8-72) Reproduction of completed page authorized DISCLAIMER The contents of this report reflect the views of the authors and do not necessarily reflect the official views or policies of the Florida Department of Transportation. This report does not constitute a standard, specification, or regulation. ACKNOWELDGEMENTS This study was sponsored by a grant from the FDOT Research Center. The support from the Research Center is gratefully acknowledged. The authors would like to thank the Project Manager, Mr. Shi-Chinag Li, Senior Transportation Planner, Planning and Environmental Management Office, FDOT District 4, for his guidance and support throughout the research. The authors also thank Mr. Michael Neidhart, Chair of the Florida Statewide Model Task Force Trip Distribution Committee and Senior Transportation Planner of Volusia Metropolitan Planning Organization, for his valuable input and guidance. The authors are grateful to the Florida Statewide Model Task Force, whose members have helped direct the research to its successful completion. The authors thank Mr. Sam Yang, Ms. Xuemei Liu, and Ms. Fang Mei, Graduate Research Assistants of the Lehman Center for Transportation Research during this project, for providing assistance in developing the models described in this report. i TABLE OF CONTENTS LIST OF TABLES.......................................................................................................................... v LIST OF FIGURES ...................................................................................................................... iix EXECUTIVE SUMMARY ......................................................................................................... xxi 1. INTRODUCTION .............................................................................................................. 1 2. LITERATURE REVIEW................................................................................................... 3 2.1 Growth Factor Methods .......................................................................................... 3 2.2 Gravity Models....................................................................................................... 4 2.3 Opportunity Models................................................................................................ 5 2.4 Destination Choice Models..................................................................................... 6 2.5 FSUTMS Trip Distribution..................................................................................... 7 2.5.1 Existing Methodology................................................................................. 8 2.5.2 Recent Model Enhancements...................................................................... 9 2.6 Modeling Practices in Other Urban Areas.............................................................. 9 2.6.1 Metropolitan Atlanta Region .................................................................... 10 2.6.2 Metropolitan Baltimore Region ................................................................ 10 2.6.3 Metropolitan Boston Region..................................................................... 11 2.6.4 Dallas-Fort Worth Metropolitan Area ...................................................... 11 2.6.5 Metropolitan Denver Region .................................................................... 11 2.6.6 Metropolitan Detroit Region..................................................................... 11 2.6.7 Huston-Galveston Region......................................................................... 12 2.6.8 New York Metropolitan............................................................................ 12 2.6.9 Metropolitan Phoenix Region................................................................... 12 2.6.10 Metropolitan Seattle Region ..................................................................... 13 2.6.11 Metropolitan St. Louis Region.................................................................. 13 2.6.12 Metropolitan Washington, D.C. Region ................................................... 13 2.6.13 Metropolitan Portland Area ...................................................................... 13 2.6.14 Metropolitan San Diego Region ............................................................... 14 2.6.15 San Francisco Bay Area............................................................................ 14 2.6.16 Sacramento Area Council of Governments .............................................. 15 2.7 Potential Trip Distribution Enhancements............................................................ 15 2.7.1 Level I Enhancements............................................................................... 15 2.7.2 Level II Enhancements ............................................................................. 16 2.7.3 Level III Enhancements ............................................................................ 17 2.8 Summary............................................................................................................... 21 ii 3. MODEL EVALUATION CRITERIA.............................................................................. 23 4. DEVELOPMENT OF INTERVENING OPPORTUNITY MODELS............................. 26 4.1 Intervening Opportunity Model Formulation ....................................................... 26 4.2 Intervening Opportunity Model Calibration ......................................................... 29 4.3 Evaluation of Intervening Opportunity Models.................................................... 30 4.4 Summary and Discussions .................................................................................... 41 5. DEVELOPMENT OF ENHANCED GRAVITY MODELS ..........................................
Load more

Recommended publications
  • 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]
  • MAPA-2050-LRTP-Appendix-C-Travel-Demand-Model-Documentation.Pdf
    Travel Demand Model | 2050 LRTP Table of Contents Introduction Figure C1: MAPA Transportation Management Area TMIP Peer Review 2010 Federal Certification Review Model Structure Figure C2: Four-Step Travel Demand Model Structure Enhanced Model Structure Figure C3: Multimodal Travel Demand Model Structure Existing Network and SE Data Traffic Analysis Zones Figure C4: Traffic Analysis Zones Socio-Economic Data Table C1: Historic Population Trends in the MAPA TMA Table C2: Forecasted Population Growth in the MAPA Region, 2010 to 2050 Figure C5: Change in Single Family Households Figure C6: Change in Employment Network Updates Table C3 - Road Network Fields Figure C7 - Count Locations External Analysis Update Figure C8 - External Station Locations Model Features Time of Day Non-Motorized Trips Figure C9 - Binomial Logit Model Figure C10 - Modeled Walk/Bike Mode Shares Compared to Weighted NHTS Data Income Based Trip Distribution Figure C11: HBW Trip Distribution – Model vs. Weighted NHTS 1 | ​MAPA Long Range Transportation Plan 2050 Figure C12: HBNW Trip Distribution – Model vs. Weighted NHTS Figure C13: NHB Trip Distribution – Model vs. Weighted NHTS Mode Choice Figure C14: Base Year Transit Network Truck Demand Module Access to Jobs Calibration and Validation Trip Generation/Distribution Table C4 - Unbalanced Production and Attraction Ratios Table C5 - HBSH Trip Rate Updates Table C6 - HBSch Trip Rate Updates Table C7 - Trips Per Household Comparison Trip Distribution Validation Checks and Calibration Adjustments Figure C15 - HBW Friction
    [Show full text]
  • Exploring the Positive Utility of Travel and Mode Choice
    Portland State University PDXScholar Dissertations and Theses Dissertations and Theses Summer 7-12-2017 Exploring the Positive Utility of Travel and Mode Choice Patrick Allen Singleton Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Transportation Commons Let us know how access to this document benefits ou.y Recommended Citation Singleton, Patrick Allen, "Exploring the Positive Utility of Travel and Mode Choice" (2017). Dissertations and Theses. Paper 3780. https://doi.org/10.15760/etd.5664 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Exploring the Positive Utility of Travel and Mode Choice by Patrick Allen Singleton A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil and Environmental Engineering Dissertation Committee: Kelly J. Clifton, Chair Jennifer Dill Liming Wang Cynthia D. Mohr Portland State University 2017 © 2017 Patrick Allen Singleton Abstract Why do people travel? Underlying most travel behavior research is the derived- demand paradigm of travel analysis, which assumes that travel demand is derived from the demand for spatially separated activities, traveling is a means to an end (reaching destinations), and travel time is a disutility to be minimized. In contrast, the “positive utility of travel” (PUT) concept suggests that travel may not be inherently disliked and could instead provide benefits or be motivated by desires for travel-based multitasking, positive emotions, or fulfillment.
    [Show full text]
  • Developing a Regional Approach to Transportation Demand Management and Nonmotorized Transportation: Best Practice Case Studies
    DEVELOPING A REGIONAL APPROACH TO TRANSPORTATION DEMAND MANAGEMENT AND NONMOTORIZED TRANSPORTATION: BEST PRACTICE CASE STUDIES June 2013 Prepared for: U.S. Department of Transportation Office of Planning, Environment, and Realty Federal Highway Administration Prepared by: U.S. Department of Transportation Research and Innovative Technology Administration John A. Volpe National Transportation Systems Center Notice This document is distributed by the U.S. Department of Transportation, in the interest of information exchange. The United States Government assumes no liability for its contents or use thereof. If trade or manufacturer’s name or products are mentioned, it is because they are considered essential to the objective of the publication and should not be considered as an endorsement. The United States Government does not endorse products or manufacturers. Quality Assurance Statement The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. Acknowledgements The John A. Volpe National Transportation Systems Center of the U.S. Department of Transportation, Research and Innovative Technology Administration, prepared this report for the Federal Highway Administration, Office of Planning. William M. Lyons of the Transportation Planning Division manages the best practices in transportation planning research project for the FHWA Office of Planning and managed development of this report. Other members of the Volpe Center project team for this report were Jared Fijalkowski and Kevin McCoy, the lead analysts.
    [Show full text]
  • Transportation Planning Models: a Review
    National Conference on Recent Trends in Engineering & Technology Transportation Planning Models: A Review Kevin B. Modi Dr. L. B. Zala Dr. F. S. Umrigar Dr. T. A. Desai M.Tech (C) TSE student, Associate Professor, Principal, Professor and Head of Civil Engg. Department, Civil Engg. Department, B. V. M. Engg. College, Mathematics Department, B. V. M. Engg. College, B. V. M. Engg. College, Vallabh Vidyamagar, India B. V. M. Engg. College, Vallabh Vidyamagar, India Vallabh Vidyamagar, India [email protected] Vallabh Vidyamagar, India [email protected] [email protected] [email protected] Abstract- The main objective of this paper is to present an the form of flows on each link of the horizon-year networks as overview of the travel demand modelling for transportation recorded by Pangotra, P. and Sharma, S. (2006), “Modelling planning. Mainly there are four stages model that is trip Travel Demand in a Metropolitan City”. In the present study, generation, trip distribution, modal split and trip assignment. Modelling is an important part of any large scale decision The choice of routes in the development of transportation making process in any system. Travel demand modelling aims planning depends upon certain parameters like journey time, distance, cost, comfort, and safety. The scope of study includes to establish the spatial distribution of travel explicitly by the literature review and logical arrangement of various models means of an appropriate system of zones. Modelling of used in Urban Transportation Planning. demand thus implies a procedure for predicting what travel decisions people would like to make given the generalized Keywords- transportation planning; trip generation;trip travel cost of each alternatives.
    [Show full text]
  • How Urban Form Characteristics at Both Trip Ends Influence Mode
    sustainability Article How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area Arefeh Nasri 1,* and Lei Zhang 2 1 National Center for Smart Growth Research and Education, University of Maryland, College Park, MD 20742, USA 2 Maryland Transportation Institute, University of Maryland, College Park, MD 20742, USA; [email protected] * Correspondence: [email protected] Received: 27 March 2019; Accepted: 14 June 2019; Published: 20 June 2019 Abstract: Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination.
    [Show full text]
  • Calibration of Nested-Logit Mode-Choice Models for Florida
    Final Report Calibration of Nested-Logit Mode-Choice Models for Florida By Mohamed Abdel-Aty, Ph.D., PE Associate Professor and Hassan Abdelwahab Ph.D. Candidate Department of Civil & Environmental Engineering University of Central Florida P.O.Box 162450 Orlando, Florida 32816-2450 Phone : 407 823-5657 Fax : 407 823-3315 Email : [email protected] November 2001 EXECUTIVE SUMMARY This report describes the development of mode choice models for Florida. Data from the 1999 travel survey conducted in Southeast Florida were used in the calibration of the models. The calibration also involved the travels times and costs of the highway and transit systems obtained from the skim files of the southeast model. The mode choice model was estimated as a three- level nested logit structure. There were three separate trip purposes calibrated. These purposes were: home based work trips (HBW), home based non-work trips (HBNW), and non home-based trips (NHB). Two separate surveys were used in the estimation process. The first is the on-board transit survey, and the second is the household survey. The portion of the nesting structure that include the different transit alternatives (the transit branch) was estimated using the on-board transit data, while the upper nest that include the choice of transit versus highway used the household travel data. This approach was used because of the very small percentage of transit trips in the household survey, and to avoid enriching the household sample, which would lead to the necessity of adjusting the coefficient estimates. The two models were linked through the use of the inclusive value of transit.
    [Show full text]
  • A Joint Modeling Analysis of Passengers' Intercity Travel
    Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2016, Article ID 5293210, 10 pages http://dx.doi.org/10.1155/2016/5293210 Research Article A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China Yanli Wang,1 Bing Wu,1 Zhi Dong,2 and Xin Ye1 1 Key Laboratory of Road and Traffic Engineering of Ministry of Education, School of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China 2School of Highway, Chang’an University, Xi’an 710064, China Correspondence should be addressed to Xin Ye; [email protected] Received 17 March 2016; Revised 14 June 2016; Accepted 20 June 2016 Academic Editor: Yakov Strelniker Copyright © 2016 Yanli Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work- related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from various travel modes in the lower level are summarized into a composite utility, which is then specified into the destination choice model as an intercity impedance factor.
    [Show full text]
  • White Paper Series Bicycle and Pedestrian Forecasting Tools: State
    White Paper Series Bicycle and Pedestrian Forecasting Tools: State of the Practice April 2015 Alisar Aoun, Julie Bjornstad, Brooke DuBose, Meghan Mitman, and Mollie Pelon, Fehr & Peers For: Federal Highway Administration DTFHGI-11-H-00024 www.pedbikeinfo.org This material is based upon work supported by the Federal Highway Administration under Cooperative Agreement No. DTFH610110H-00024. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the Author(s) and do not necessarily reflect the view of the Federal Highway Administration. Introduction Transportation forecasting models predict levels of activity, and help inform decisions on issues such as future facility use and the prioritization of projects. Travel and demand forecasting methods have long been used to estimate the number of vehicles traveling on a specific street or network and to estimate ridership for mass transit. Many jurisdictions and metropolitan planning organizations use forecasting methods to determine the potential impact of new development, changes to roadway capacity, or projected ridership for new transit. However, these methods have traditionally excluded pedestrian and bicycle activity. For communities seeking to support walking and bicycling activity, quantifying the use and potential demand of facilities that support active transportation is increasingly important. To meet this need, bicycle and pedestrian forecasting models are being developed and integrated into planning projects focusing on facilitating mobility, managing resources, and improving health and safety. These emerging forecasting approaches vary widely in the amount of data and level of effort required. The type, specificity, and reliability of data also vary between different forecasting approaches. For example, data used in forecasting models can range from readily available U.S.
    [Show full text]
  • Mode Choice Modeling Using Artificial Neural Networks by Praveen
    Mode Choice Modeling Using Artificial Neural Networks by Praveen Kumar Edara Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering Dušan Teodorović, Chair John Collura Antonio Trani October 2003 Falls Church, Virginia Key words: Mode choice, Neural networks, Multinomial logit, Linear regression, Clustering, American travel survey database Mode Choice Modeling Using Artificial Neural Networks Praveen Edara ABSTRACT Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data become available. The primary goal of this thesis is to develop mode choice models using artificial neural networks and compare the results with traditional mode choice models like the multinomial logit model and linear regression method. The data used for this modeling is extracted from the American Travel Survey data. Data mining procedures like clustering are used to process the extracted data. The results of three models are compared based on residuals and error criteria. It is found that neural network approach produces the best results for the chosen set of explanatory variables. The possible reasons for such results are identified and explained to the extent possible. The three major objectives of this thesis are to: present an approach to handle the data from a survey database, address the mode choice problem using artificial neural networks, and compare the results of this approach with the results of traditional models vis-à-vis logit model and linear regression approach.
    [Show full text]
  • SANDAG Travel Demand Model Documentation
    Technical Appendix 15 SANDAG Travel Demand Model Documentation Appendix Contents SANDAG Travel Demand Model Documentation ............................ TA 15-2 2050 Regional Transportation Plan SANDAG Travel Demand . SANDAG Transportation Model Sensitivity Model Documentation Analysis and Report: This report provides insight into the sensitivity and elasticity of Technical Appendix 15 includes several the SANDAG four-step travel demand reports that document the SANDAG Travel model in terms of policy and operational Demand Model, as follows: adjustment. 2050 Regional Travel Demand Model Documentation: This report presents a description of the components of the SANDAG travel demand model used in the 2050 Regional Transportation Plan, including a general flow of information and some of the key inputs, assumptions, and computations for each of the components. SANDAG Off-Model Greenhouse Gas Reduction Methodology for the 2050 Regional Transportation Plan: This paper documents the SANDAG methodology for estimating the off-model greenhouse gas (GHG) reductions for several strategies included in the 2050 Regional Transportation Plan (RTP). These strategies include aspects of the bicycle, pedestrian, safe routes to schools, vanpool, carpool, and buspool programs. SANDAG Travel Demand Model Validation Report: This report provides a guide to the accuracy of the SANDAG transportation model compared to observed data in and around 2008. It also provides detailed technical information on how the transportation model was used to support the development
    [Show full text]
  • Reasons Why Bicycling and Walking Are Not Being Used More
    Publication No. FHWA-PD-92-041 C•ase Study No. 1 Reasons Why Bicycling And Walking Are And Are Not Being Used More Extensively As Travel Modes U.S. Department National Bicycling of Transportation Federal Highway And Walking Study Administration FHWA NATIONAL BICYCLING & WALKING STUDY CASE STUDY #1 REASONS WHY BICYCLING AND WALKING ARE AND ARE NOT BEING USED MORE EXTENSIVELY AS TRAVEL MODES TABLE OF CONTENTS EXECUTIVE SUMMARY..........................................................................................................1 Introduction ..............................................................................................................................4 Purpose.............................................................................................................................4 Scope and organization.....................................................................................................4 Chapter I. The Individual Choice to Bicycle & Walk...................................................................6 A. Personal and Subjective Factors..............................................................................6 B. Objective Factors.....................................................................................................10 1. Environmental .............................................................................................10 2. Infrastructural Features................................................................................11 C. Factors Specific to Walking .....................................................................................12
    [Show full text]