A tour-based analysis of travel mode choice accounting for regional transit service

Chuan Ding , Yao-yu Lin , Bing-lei Xie , Xiao-yu Zhu , Sabyasachee Mishra

Journal of Central South University ›› 2015, Vol. 22 ›› Issue (1) : 402 -408.

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Journal of Central South University ›› 2015, Vol. 22 ›› Issue (1) : 402 -408. DOI: 10.1007/s11771-015-2535-5
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A tour-based analysis of travel mode choice accounting for regional transit service

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Abstract

The aim of this work is to explore the impact of regional transit service on tour-based commuter travel behavior by using the Bayesian hierarchical multinomial logit model, accounting for the spatial heterogeneity of the people living in the same area. With two indicators, accessibility and connectivity measured at the zone level, the regional transit service is captured and then related to the travel mode choice behavior. The sample data are selected from Washington-Baltimore Household Travel Survey in 2007, including all the trips from home to workplace in morning hours in Baltimore city. Traditional multinomial logit model using Bayesian approach is also estimated. A comparison of the two different models shows that ignoring the spatial context can lead to a misspecification of the effects of the regional transit service on travel behavior. The results reveal that improving transit service at regional level can be effective in reducing auto use for commuters after controlling for socio-demographics and travel-related factors. This work provides insights for interpreting tour-based commuter travel behavior by using recently developed methodological approaches. The results of this work will be helpful for engineers, urban planners, and transit operators to decide the needs to improve regional transit service and spatial location efficiently.

Keywords

transit service / travel mode choice / spatial heterogeneity / Bayesian hierarchical model / transit accessibility / transit connectivity / tour

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Chuan Ding, Yao-yu Lin, Bing-lei Xie, Xiao-yu Zhu, Sabyasachee Mishra. A tour-based analysis of travel mode choice accounting for regional transit service. Journal of Central South University, 2015, 22(1): 402-408 DOI:10.1007/s11771-015-2535-5

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References

[1]

MishraS, WelchT F, JhaM K. Performance indicators for public transit connectivity in multi-modal transportation networks [J]. Transportation Research Part A, 2012, 46(7): 1066-1085

[2]

HensherD A, StopherP, BullockP. Service quality-Developing a service quality index in the provision of commercial bus contracts [J]. Transportation Research Part A, 2003, 37(6): 499-517

[3]

SpitzG, GreeneE, AdlerT, DallisonR. Qualitative and quantitative approaches for studying transit stations [C]. Transportation Research Board 86th Annual Meeting. Washington, DC, 20071-11

[4]

BeimbornE A, GreenwaldM J, JinX. Accessibility, connectivity, and captivity: Impacts on transit choice [J]. Journal of the Transportation Research Record, 2003, 1835: 1-9

[5]

GouliasK G. Multilevel analysis of daily time use and time allocation to activity types accounting for complex covariance structures using correlated random effects [J]. Transportation, 2002, 29(1): 31-48

[6]

BhatC R. A multi-level cross-classified model for discrete response variables [J]. Transportation Research Part B, 2000, 34(7): 567-582

[7]

HongJ, ShenQ, ZhangL. How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales [J]. Transportation, 2014, 41(3): 419-440

[8]

ClarkA F, ScottD M, YiannakouliasN. Examining the relationship between active travel, weather, and the built environment: A multilevel approach using a GPS-enhanced dataset [J]. Transportation, 2014, 41(2): 325-338

[9]

HongJ, ShenQ. Residential density and transportation emissions: Examining the connection by addressing spatial autocorrelation and self-selection [J]. Transportation Research Part D, 2013, 22: 75-79

[10]

ShuttleworthI, GouldM. Distance between home and work: A multilevel analysis of individual workers, neighborhoods, and employment sites in Northern Ireland [J]. Environment and Planning A, 2010, 42(5): 1221-1238

[11]

AntipovaA, WangF, WilmotC. Urban land uses, socio-demographic attributes and commuting: A multilevel modeling approach [J]. Applied Geography, 2011, 31(3): 1010-1018

[12]

KrizekK J. Neighborhood services, trip purpose, and tour-based travel [J]. Transportation, 2003, 30(4): 387-410

[13]

MillerE J, RoordaM J, CarrascoJ A. A tour-based model of travel mode choice [J]. Transportation, 2005, 32(4): 399-422

[14]

ChenC, GongH, PaaswellR. Role of the built environment on mode choice decisions: Additional evidence on the impact of density [J]. Transportation, 2008, 35(3): 285-299

[15]

AckerV V, WitloxF. Commuting trips within tours: How is commuting related to land use? [J]. Transportation, 2011, 38(3): 465-486

[16]

LevinsonD M. Accessibility and the journey to work [J]. Journal of Transport Geography, 1998, 6(1): 11-21

[17]

HadasY, CederA. Public transit network connectivity: spatial-based performance indicators [J]. Journal of the Transportation Research Record, 2010, 2143: 1-8

[18]

BorgattiS P. Centrality and network flow [J]. Social Network, 2005, 27(1): 55-71

[19]

ParkJ, KangS C. A model for evaluating the connectivity of multimodal transit networks [C]. Transportation Research Board 90th Annual Meeting. Washington, DC, 20111-10

[20]

SnijdersT A B, BoskerRMultilevel analysis: an introduction to basic and advanced multilevel modeling (Second edition) [M], 2012, Thousand Oaks, California, Sage: 77-93

[21]

LawsonA BBayesian disease mapping: Hierarchical modeling in spatial epidemiology [M], 2008, Boca Ration, New York, CRC Press: 201-223

[22]

CongdonPBayesian statistical modelling (Second edition) [M], 2001, New York, Wiley: 37-52

[23]

GelmanA, PardoeI. Bayesian measures of explained variance and pooling multilevel (hierarchical) models [J]. Technometrics, 2006, 48(2): 241-251

[24]

SchwanenT, DijstM, DielemanF M. A microlevel analysis of residential context and travel time [J]. Environment and Planning A, 2002, 34(8): 1487-1507

[25]

YaoX-h, ZhanF B, LuY-m, YangM-hua. Effects of real-time traffic information systems on traffic performance under different network structures [J]. Journal of Central South University, 2012, 19(2): 586-592

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