Mechanism Research of Bus Dynamic Information Impact on the Commuter Travel Behavior

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In this paper, according to the characteristics of the bus travel decision-making, the traffic behavior selection data in Nanjing were collected by designing traffic wishes questionnaires and a binary logit model was built on dynamic information service under the bus commuters travel route choice behavior of binary logit model. This paper analyses the effect by using the model parameter calibration including bus-taking time, bus congestion and personal information e.g. age and gender on the bus commuters travel route choice behavior. Studies have shown that public transport information are closely related to travel routes and travel activities, and bus commuters will make adjustments on travel route after obtaining travel information. Public transportation information can change the passengers’ state of participating in transportation and improve the level of the public transport system service in some ways.

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660-666

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March 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] Eluru N, Chakour V, El-Geneidy A M. Travel mode choice and transit route choice behavior in Montreal: insights from McGill University members commute patterns[J]. Public Transport, 2012, 4(2): 129-149.

DOI: 10.1007/s12469-012-0056-2

Google Scholar

[2] PratoC G, Bekhor S, Pronello C. Latent variables and route choice behavior[J]. Transportation, 2012, 39(2): 299-319.

DOI: 10.1007/s11116-011-9344-y

Google Scholar

[3] Zeng Y, Li J, Zhu H. Passenger Route Choice Behavior with Congestion Consideration[J]. Applied Mechanics and Materials, 2013, 368: 1876-1880.

DOI: 10.4028/www.scientific.net/amm.368-370.1876

Google Scholar

[4] Yao L Y, Sun L S, Guan H Z. Study on Modal Split Method Based on Nested Logit Model[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2010, 34(4): 738-741.

Google Scholar

[5] Chen J F, Xu L J. Research on the Best Travel Path Selecting Based on the Passenger Psychology[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2012, 36(3): 034.

Google Scholar

[6] Zhou W, Zhao S C. Quantitative analysis of traveler route choice behavior based on Mixed Logit model[J]. Journal of Jilin University(Engineering and Technology Edition), 2013 , 43(2): 304-309.

Google Scholar

[7] Chen J, Yan Q P, Yang F, et al. SEM-Logit Integration Model of Travel Mode Choice Behaviors[J]. Journal of South China University of Technology(Natural Science Edition), 2013, 41(2): 51-57.

Google Scholar

[8] Guan H Z. Disaggregate Model-Traffic Behavior Analysis Tool[M]. China Communications Press, (2004).

Google Scholar

[9] Zhang T R, Yang D Y, Zhao Y L, et al. Comparative Study of RP/SP Combined Data Estimation Between Mixed Logit and Nested Logit Model[J]. Journal of Tongji University (Natural Science), 2008, 36 (8): 1073-1078.

Google Scholar