Metropolis-Hasting based Expanded Path Size Logit model for cyclists’ route choice using GPS data

Abstract This study contributes to the field of cycling route choice by adopting the unprecedented combination of the Metropolis-Hastings (MH) path-sampling algorithm and the Expanded Path Size Logit (EPSL) model. The MH sampling approach is used to generate 15 alternative route choice sets for cyclists. The EPSL multivariate route choice framework is utilized to account for the correlation between sampled and non-sampled alternatives (joint MH-EPSL model). The data used in this paper is drawn from GPS data collected by the City of Toronto using a custom-built smartphone application in 2014–2015. The study focuses on non-work-related cycling trips (shopping, leisure, social and others) in downtown Toronto on weekdays. The estimated results indicate that the presence of bicycle lanes and road medians attractions and number of trees along the path have a positive impact on cyclist route choice. In general, cyclists prefer to take shorter routes on lower speed roads with less public transit stops especially during the evening rush hour, and less willing to take one-way streets, local roads, and steep road segments. These findings are useful to policy makers as well as transportation and urban designers when developing a cycling network aiming to attract more cyclists. Finally, our results indicate that the MH-EPSL model performance is an appropriate framework to study cyclists’ route choice decisions.

[1]  Shlomo Bekhor,et al.  Methodological transferability in route choice modeling , 2009 .

[2]  S. Handy.  Regional Versus Local Accessibility: Neo-Traditional Development and Its Implications for Non-work Travel , 1992 .

[3]  Brian Caulfield,et al.  Determining bicycle infrastructure preferences – A case study of Dublin , 2012 .

[4]  R. Golledge.  A Three-step General Map Matching Method in the GIS Environment: Travel/Transportation Study Perspective , 2006 .

[5]  M. Bierlaire,et al.  Metropolis-Hastings sampling of paths , 2013 .

[6]  Billy Charlton,et al.  A GPS-based bicycle route choice model for San Francisco, California , 2011 .

[7]  Daniel McFadden,et al.  Modelling the Choice of Residential Location , 1977 .

[8]  Sivaramakrishnan Srinivasan,et al.  Route Choice Modeling Using GPS-Based Travel Surveys , 2013 .

[9]  M. Boarnet,et al.  Can Land Use Policy Really Affect Travel Behavior? A Study of the Link between Non-Work Travel and Land Use Characteristics , 1996 .

[10]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[11]  Kenneth A. Small,et al.  The Value of Time and Reliability: Measurement from a Value Pricing Experiment , 2001 .

[12]  A. M. Lockwood,et al.  On Distinguishing Between Physically Active and Physically Passive Episodes and Between Travel and Activity Episodes: An Analysis of Weekend Recreational Participation in the San Francisco Bay Area , 2004 .

[13]  T. Gärling,et al.  Spatial Behavior in Transportation Modeling and Planning , 2001 .

[14]  Marlon G. Boarnet,et al.  The influence of land use on travel behavior: specification and estimation strategies , 2001 .

[15]  William E. Moritz,et al.  Survey of North American Bicycle Commuters: Design and Aggregate Results , 1997 .

[16]  Carlo G. Prato,et al.  Route choice modeling: past, present and future research directions , 2009 .

[17]  Gulsah Akar,et al.  Influence of Individual Perceptions and Bicycle Infrastructure on Decision to Bike , 2009 .

[18]  M. Bierlaire,et al.  Sampling of Alternatives for Route Choice Modeling , 2009 .

[19]  Andrew Daly,et al.  Choice Modelling: The State-of-the-art and the State-of-practice: Proceedings from the Inaugural International Choice Modelling Conference , 2010 .

[20]  John L. Renne,et al.  Socioeconomics of Urban Travel: Evidence from the 2001 NHTS , 2003 .

[21]  Eric J. Miller,et al.  Simulations of firm location decisions: Replicating office location choices in the Greater Toronto Area , 2015 .

[22]  Chandra R. Bhat,et al.  Commuter Bicyclist Route Choice: Analysis Using a Stated Preference Survey , 2003 .

[23]  Jeremy N. Bailenson,et al.  Road Climbing: Principles Governing Asymmetric Route Choices on Maps , 1998 .

[24]  Fred L. Mannering.  Poisson analysis of commuter flexibility in changing routes and departure times , 1989 .

[25]  Chandra R. Bhat,et al.  A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models , 2006 .

[26]  Lawrence D. Frank,et al.  Active transportation and physical activity: opportunities for collaboration on transportation and public health research , 2004 .

[27]  Bilal Farooq,et al.  On the role of bridges as anchor points in route choice modeling , 2018 .

[28]  Xiaokuan Yang,et al.  Random Parameter Nested Logit Model for Combined Departure Time and Route Choice , 2015 .

[29]  Wei Fan,et al.  Dynamic Travel Time Prediction Models for Buses Using Only GPS Data , 2015 .

[30]  Piet H. L. Bovy,et al.  STOCHASTIC ROUTE CHOICE SET GENERATION: BEHAVIORAL AND PROBABILISTIC FOUNDATIONS , 2007 .

[31]  Chandra R. Bhat,et al.  An analysis of bicycle route choice preferences in Texas, US , 2009 .

[32]  Cathy L. Antonakos,et al.  ENVIRONMENTAL AND TRAVEL PREFERENCES OF CYCLISTS. , 1993 .

[33]  Shlomo Bekhor,et al.  EFFECTS OF CHOICE SET SIZE AND ROUTE CHOICE MODELS ON PATH-BASED TRAFFIC ASSIGNMENT , 2008 .

[34]  Khandker Nurul Habib,et al.  Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception, consciousness and comfortability on the purpose of biking and bike ownership , 2014 .

[35]  Carlo G. Prato,et al.  The Factor of Revisited Path Size , 2008 .

[36]  C. Bhat.  Analysis of travel mode and departure time choice for urban shopping trips , 1998 .

[37]  Chandra R. Bhat,et al.  An Analysis of Weekend Work Activity Patterns in the San Francisco Bay Area , 2007 .

[38]  M. Bierlaire,et al.  Discrete Choice Methods and their Applications to Short Term Travel Decisions , 1999 .

[39]  M. Ben-Akiva,et al.  EMPIRICAL TEST OF A CONSTRAINED CHOICE DISCRETE MODEL : MODE CHOICE IN SAO PAULO, BRAZIL , 1987 .

[40]  E. Cascetta,et al.  A MODIFIED LOGIT ROUTE CHOICE MODEL OVERCOMING PATH OVERLAPPING PROBLEMS. SPECIFICATION AND SOME CALIBRATION RESULTS FOR INTERURBAN NETWORKS , 1996 .

[42]  Nebiyou Tilahun,et al.  Trails, Lanes, or Traffic: Value of Different Bicycle Facilities Using Adaptive Stated-Preference Survey , 2008 .

[43]  Naveen Eluru,et al.  Evolution of Adults’ Weekday Time Use Patterns from 1992 to 2010: A Canadian Perspective , 2014 .

[44]  Jennifer Dill,et al.  Where do cyclists ride? A route choice model developed with revealed preference GPS data , 2012 .

[45]  C. Chorus.  Regret theory-based route choices and traffic equilibria , 2012 .

[46]  J. Pucher,et al.  Bicycling renaissance in North America? Recent trends and alternative policies to promote bicycling , 1999 .

[47]  Meng Li,et al.  A regret theory-based route choice model , 2017 .

[48]  Ps Hu.  SUMMARY OF TRAVEL TRENDS: 2001 NATIONAL HOUSEHOLD TRAVEL SURVEY , 2004 .

[49]  Gwo-Hshiung Tzeng,et al.  Using a weight-assessing model to identify route choice criteria and information effects , 2001 .

[50]  Chandra R. Bhat,et al.  Modeling the Generation and Organization of Household Activity Stops , 1999 .

[51]  Darren M. Scott,et al.  A GIS-based toolkit for route choice analysis , 2011 .

[52]  Anders Karlström,et al.  A link based network route choice model with unrestricted choice set , 2013 .

[53]  H. Oliver Gao,et al.  An advanced traveler navigation system adapted to route choice preferences of the individual users , 2017 .

[54]  John E Abraham,et al.  Influences on bicycle use , 2007 .