Modeling Travel Mode Choices in Connection to Metro Stations by Mixed Logit Models: A Case Study in Nanjing, China

  • Jingxian Wu School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Southeast University; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies.
  • Min Yang School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Southeast University; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies.
  • Shangjue Sun School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Southeast University; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies.
  • Jingyao Zhao Nanjing Institute of City& Transport Planning Co., Ltd; F10-12, Transport Building, 63 Zhujiang, Nanjing, 210096, P. R. China
Keywords: multimodal rail transit, access and egress, mode choice, mixed logit

Abstract

Urban rail transit trips usually involve multiple stages, which can be differentiated in terms of transfers that may involve distinct access and egress modes. Most studies on access and egress mode choices of urban rail transit have separately examined the two mode choices. However, in reality, the two choices are temporally correlated. This study, therefore, has sequentially applied the mixed logit to examine the contributors of access and egress mode choices of urban metro commuters using the data from a recent survey conducted in Nanjing, China. 9 typical multimodal combinations constituted by 5 main access modes (walk, bike, electric bike, bus, and car) and 2 main egress modes (walk and bus) are included in the study. The result proves that the model is reliable and reproductive in analyzing access/egress mode choices of metro commuters. Estimation results prove the existence of time constraint and service satisfaction effect of access trip on commuters’ egress mode choice and reveal the importance of transfer infrastructure and environments that serve for biking, walking, bus riding, and car parking in commuter’s connection choice. Also, policy implications are segmentally concluded for the transfer needs of commuters in different groups to encourage the use of metro multimodal trips.

Author Biographies

Jingxian Wu, School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Southeast University; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies.
Jingxian Wu is a Ph.D. Candidate in the school of transportation at Southeast University. Her interest focuses on travel behavior and public transit service.
Min Yang, School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Southeast University; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies.

Dr. Min Yang is a professor in the School of Transportation at Southeast University.  his research focus is diverse, which includes transportation demand and behavioral analysis, urban transportation planning. he has published many papers in many well-known international journals.

Shangjue Sun, School of Transportation, Southeast University; Jiangsu Key Laboratory of Urban ITS, Southeast University; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies.
Shangjue Sun is the graduate research assistant in the school of transportation at Southeast University. her interest is in the field of transportation and travel behavior.
Jingyao Zhao, Nanjing Institute of City& Transport Planning Co., Ltd; F10-12, Transport Building, 63 Zhujiang, Nanjing, 210096, P. R. China
Jingyao Zhao is a master who graduated from Southeast University and now is working at Nanjing Insitute of City& Transport Planning Co., Ltd. Her interest is in the travel behavior and rail transit service.

References

[1] Clifton K, Muhs C. Capturing and representing multimodal trips in travel surveys: Review of the practice. Transportation Research Record: Journal of the Transportation Research Board. 2012;2285: 74-83.
[2] Givoni M, Rietveld P. The access journey to the railway station and its role in passengers’ satisfaction with rail travel. Transport Policy. 2007;14(5): 357-365.
[3] Guo Z, Wilson NHM. Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground. Transportation Research Part A: Policy and Practice. 2011;45(2): 91-104.
[4] Heinen E, Bohte W. Multimodal commuting to work by public transport and bicycle: Attitudes toward mode choice. Transportation Research Record: Journal of the Transportation Research Board. 2014;2468: 111-122.
[5] Guo Z, Wilson N. Modeling effects of transit system transfers on travel behavior: case of commuter rail and subway in Downtown Boston, Massachusetts. Transportation Research Record: Journal of the Transportation Research Board. 2007;2006: 11-20.
[6] Andersson DE, Shyr OF, Lee A. The successes and failures of a key transportation link: accessibility effects of Taiwan’s high-speed rail. The Annals of Regional Science. 2012;48(1): 203-223.
[7] Milakis D, Cervero R, Van Wee B, Maat K. Do people consider an acceptable travel time? Evidence from Berkeley, CA. Journal of Transport Geography. 2015;44: 76-86.
[8] Krygsman S, Dijst M, Arentze T. Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio. Transport Policy. 2004;11(3): 265-275.
[9] Cherry T, Townsend C. Assessment of potential improvements to metro-bus transfers in Bangkok, Thailand. Transportation Research Record: Journal of the Transportation Research Board. 2012;2276: 116-122.
[10] Duncan M, Cook D. Is the provision of park-and-ride facilities at light rail stations an effective approach to reducing vehicle kilometers traveled in a US context?. Transportation Research Part A: Policy and Practice. 2014;66: 65-74.
[11] Yang M, Zhao J, Wang W, Liu Z, Li Z. Metro commuters’ satisfaction in multi-type access and egress transferring groups. Transportation Research Part D: Transport and Environment. 2015;34: 179-194.
[12] Brons M, Givoni M, Rietveld P. Access to railway stations and its potential in increasing rail use. Transportation Research Part A: Policy and Practice. 2009;43(2): 136-149.
[13] Seaborn C, Attanucci J, Wilson N. Analyzing multimodal public transport journeys in London with smart card fare payment data. Transportation Research Record: Journal of the Transportation Research Board. 2009;2121: 55-62.
[14] Debrezion G, Pels E, Rietveld P. Modelling the joint access mode and railway station choice. Transportation Research Part E: Logistics and Transportation Review. 2009;45(1): 270-283.
[15] Zhao P, Li S. Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing. Transportation Research Part A: Policy and Practice. 2017;99: 46-60.
[16] Tran MT, Zhang J, Fujiwara A. Can We reduce the access by motorcycles to mass transit systems in future Hanoi?. Procedia-Social and Behavioral Sciences. 2014;138: 623-631.
[17] Bovy PH, Hoogendoorn-Lanser S. Modelling route choice behaviour in multi-modal transport networks. Transportation. 2005;32(4): 341-368.
[18] Arentze TA, Molin EJ. Travelers’ preferences in multimodal networks: design and results of a comprehensive series of choice experiments. Transportation Research Part A: Policy and Practice. 2013;58: 15-28.
[19] Cheng YH, Liu KC. Evaluating bicycle-transit users’ perceptions of intermodal inconvenience. Transportation Research Part A: Policy and Practice. 2012;46(10): 1690-1706.
[20] Chen L, Pel A, Chen X, Sparing D, Hansen I. Determinants of bicycle transfer demand at metro stations: Analysis of stations in Nanjing, China. Transportation Research Record: Journal of the Transportation Research Board. 2012;2276: 131-137.
[21] Zhao J, Deng W, Song Y, Zhu Y. Analysis of metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models. Transportation. 2014;41(1): 133-155.
[22] Wen CH, Wang WC, Fu C. Latent class nested logit model for analyzing high-speed rail access mode choice. Transportation Research Part E: Logistics and Transportation Review. 2012;48(2): 545-554.
[23] Yang T, Qian L. Nanjing transport annual report. Nanjing Urban Planning Bureau, Report number 2015. Chinese
[24] Cherchi E, Cirillo C. Validation and forecasts in models estimated from multiday travel survey. Transportation Research Record: Journal of the Transportation Research Board. 2010;2175: 57-64.
[25] McFadden D, Train K. Mixed MNL models for discrete response. Journal of Applied Econometrics. 2000;15: 447-470.
[26] Hess S, Train KE, Polak JW. On the use of a modified latin hypercube sampling (MLHS) method in the estimation of a mixed logit model for vehicle choice. Transportation Research Part B: Methodological. 2006;40(2): 147-163.
[27] Bierlaire M. Estimation of discrete choice models with BIOGEME 1.8. Lausanne, Switzerland: Transport and Mobility Laboratory, EPFL Publishing; 2008. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.8352&rep=rep1&type=pdf
[28] Hurtubia R, Nguyen MH, Glerum A, Bierlaire M. Integrating psychometric indicators in latent class choice models. Transportation Research Part A: Policy and Practice. 2014;64: 135-146.
Published
2018-10-31
How to Cite
1.
Wu J, Yang M, Sun S, Zhao J. Modeling Travel Mode Choices in Connection to Metro Stations by Mixed Logit Models: A Case Study in Nanjing, China. Promet - Traffic & Transportation [Internet]. 31Oct.2018 [cited 20Nov.2018];30(5):549-61. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2623
Section
Articles