Impacts of Real-Time Transit Information on Bus Passengers’ Travel Choices Based on Travel Behaviour Survey
Abstract
Real-time transit information (RTI) service can provide travellers with information on public transport and guide them to arrange departure time and travel mode accordingly. This paper aims to analyse travellers’ choices under RTI by exploring the relationship between the related variables of RTI and passengers’ travel choice. Based on the stated preference (SP) survey data, the ordinal logistic regression model is established to analyse the changing probability of passengers’ travel behaviour under RTI. The model calculation results show that travellers getting off work are more likely to change their travel choice under RTI. When data from the control and experimental groups are compared, the differences in route selection are significant. Specifically, passengers with RTI have a more complex route selection than those without, including their changes of travel mode, departure time, vehicles, and stop choices. The research findings can provide insights into the optimisation of intelligent transit information systems and the strategy of RTI. Also, the analysis of passengers’ travel choice under RTI in the transit network can help to improve network planning.
References
Brakewood C, Watkins K. A literature review of the passenger benefits of real-time transit information. Transport Reviews. 2019;39(3): 327-356. DOI: 10.1080/01441647.2018.1472147
Brakewood C, Barbeau S, Watkins K. An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida. Transportation Research Part A: Policy and Practice. 2014;69: 409-422. DOI: 10.1016/j.tra.2014.09.003
Carrel A, Halvorsen A, Walker JL. Passengers’ perception of and behavioral adaptation to unreliability in public transportation. Transportation Research Record. 2013;2351(1): 153-162. DOI: 10.3141/2351-17
Páez A, Whalen K. Enjoyment of commute: A comparison of different transportation modes. Transportation Research Part A: Policy and Practice. 2010;44(7): 537-549. DOI: 10.1016/j.tra.2010.04.003
Brakewood C, Macfarlane GS, Watkins K. The impact of real-time information on bus ridership in New York City. Transportation Research Part C: Emerging Technologies. 2015;53: 59-75. DOI: 10.1016/j.trc.2015.01.021
Lu H, et al. The impact of real-time information on passengers’ value of bus waiting time. Transportation Research Procedia. 2018;31: 18-34. DOI: 10.1016/j.trpro.2018.09.043
Carrel A, Halvorsen A, Walker JL. Passengers’ perception of and behavioral adaptation to unreliability in public transportation. Transportation Research Record. 2013;235(1): 153-162. DOI: 10.3141/2351-17
Wong J, Reed L, Watkins K, Hammond R. Open transit data: State of the practice and experiences from participating agencies in the United States. Proceedings of the 2013 Annual Meeting of the Transportation Research Board; 2013.
Fonzone A, Schmöcker JD. Effects of transit real-time information usage strategies. Transportation Research Record. 2014;2417(1): 121-129. DOI: 10.3141/2417-13
Watkins KE, et al. Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transportation Research Part A: Policy and Practice. 2011;45(8): 839-848. DOI: 10.1016/j.tra.2011.06.010
Brakewood CE. Quantifying the Impact of Real-Time Information on Transit Ridership. PhD thesis. Georgia Institute of Technology; 2014.
Liu Y, Shi J, Jian M. Understanding visitors’ responses to intelligent transportation system in a tourist city with a mixed ranked logit model. Journal of Advanced Transportation. 2017; 1-13. DOI: 10.1155/2017/8652053
Zeng Y, Li J, Zhu H. Pedestrian path selection behavior under real-time information. Journal of Computer Applications. 2013;33(10): 2964-2968. Chinese.
Mulley C, Clifton GT, Balbontin C, Ma L. Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW. Transportation Research Part A: Policy and Practice. 2017;101(C): 111-132. DOI: 10.1016/j.tra.2017.05.007
Tang L, Thakuriah PV. Ridership effects of real-time bus information system: A case study in the City of Chicago. Transportation Research Part C: Emerging Technologies. 2012;22: 146-161. DOI: 10.1016/j.trc.2012.01.001
Hu X, et al. Behavioral responses to pre-planned road capacity reduction based on smartphone GPS trajectory data: A functional data analysis approach. Journal of Intelligent Transportation Systems. 2019;23(2): 133-143. DOI: 10.1080/15472450.2018.1488133
Zhang F, Shen Q, Clifton KJ. Examination of traveler responses to real-time information about bus arrivals using panel data. Transportation Research Record. 2008;2082(1): 107-115.
Gooze A, Watkins KE, Borning A. Benefits of real-time transit information and impacts of data accuracy on rider experience. Transportation Research Record. 2013;2351(1): 95-103.
Tang L, Thakuriah P. Will the psychological effects of real-time transit information systems lead to ridership gain? Transportation Research Record. 2011;2216(1): 67-74.
Drabicki A, Kucharski R, Cats O, Fonzone A. Simulating the effects of real-time crowding information in public transport networks. 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples; 2017. p. 675-680. DOI: 10.1109/MTITS.2017.8005598
Chen Y, et al. Travel mode prediction model based on Logit of low carbon psychological latent variables. Journal of Highway and Transportation Research and Development. 2017;34(09): 100-108+137. Chinese.
Hickman MD, Wilson NHM. Passenger travel time and path choice implications of real-time transit information. Transportation Research Part C: Emerging Technologies. 1995;3(4): 211-226. DOI: 10.1016/0968-090X(95)00007-6
Cats O, Loutos G. Evaluating the added-value of online bus arrival prediction schemes. Transportation Research Part A: Policy and Practice. 2016;86: 35-55. DOI: 10.1016/j.tra.2016.02.004
Dziekan K, Kottenhof K. Dynamic at-stop real-time information displays for public transport: Effects on customers. Transportation Research Part A: Policy and Practice. 2007;41(6): 489-501. DOI: 10.1016/j.tra.2006.11.006
Tang Q, Hu X. Triggering behavior changes with information and incentives: An active traffic and demand management-oriented review. Advances in Transport Policy and Planning. 2019;3: 209-250. DOI: 10.1016/bs.atpp.2019.05.002
Ben-Elia E, Shiftan Y. Which road do I take? A learning-based model of route-choice behavior with real-time information. Transportation Research Part A: Policy and Practice. 2010;44(4): 249-264. DOI: 10.1016/j.tra.2010.01.007
Gu Y, Han Y, Fang X. Research on passenger flow forecasting method of public transport hub station based on ARMA model. Journal of Transport Information and Safety. 2011;29(02): 5-9. Chinese.
De Vos J. Do people travel with their preferred travel mode? Analyzing the extent of travel mode dissonance and its effect on travel satisfaction. Transportation Research Part A: Policy and Practice. 2018;117: 261-274. DOI: 10.1016/j.tra.2018.08.034
Copyright (c) 2021 Yajuan Deng, Mingli Chen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
