How does interchange affect passengers’ route choices in urban rail transit? – a case study of the Shanghai Metro

References

• Arentze, T. A. 2013. “Adaptive Personalized Travel Information Systems: A Bayesian Method to Learn Users’ Personal Preferences in Multimodal Transport Networks.” IEEE Transactions on Intelligent Transportation Systems 14 (4): 1957–1966. doi:https://doi.org/10.1109/TITS.2013.2270358.
   Web of Science ®Google Scholar
• Ben-Akiva, M., D. McFadden, K. Train, J. Walker, C. Bhat, M. Bierlaire, D. Bolduc, A. Boersch-Supan, D. Brownstone, and D. S. Bunch. 2002. “Hybrid Choice Models: Progress and Challenges.” Marketing Letters 13 (3): 163–175. doi:https://doi.org/10.1023/A:1020254301302.
   Web of Science ®Google Scholar
• Ben-Akiva, M., M. J. Bergman, A. J. Daly, and R. Ramaswamy, 1984. Modeling Inter-urban Route Choice Behaviour, in: Proceedings of the 9th International Symposium on Transportation and Traffic Theory. VNU Science Press Utrecht, The Netherlands, Delft, The Netherlands, pp. 299–330.
   Google Scholar
• Ben-Akiva, M. E., S. R. Lerman, and S. R. Lerman. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT press, Cambridge, Massachusetts, United States.
   Google Scholar
• Ceder, A. (Avi), and Y. Jiang. 2020. “Route Guidance Ranking Procedures with Human Perception Consideration for Personalized Public Transport Service.” Transportation Research Part C: Emerging Technologies 118: 102667. doi:https://doi.org/10.1016/j.trc.2020.102667.
   PubMed Web of Science ®Google Scholar
• Cheng, L., X. Chen, J. De Vos, X. Lai, and F. Witlox. 2019. “Applying a Random Forest Method Approach to Model Travel Mode Choice Behavior.” Travel Behaviour and Society 14: 1–10. doi:https://doi.org/10.1016/j.tbs.2018.09.002.
   Web of Science ®Google Scholar
• China Urban Rail Transit Association, 2017. Annual Statistical Report on China Urban Rail Transit 2017 [WWW Document]. http://www.camet.org.cn/index.php?m=content&c=index&a=show&catid=18&id=13532 (accessed 23 8 2019).
   Google Scholar
• Chorus, C. G., E. J. E. Molin, and B. Van Wee. 2006. “Use and Effects of Advanced Traveller Information Services (ATIS): A Review of the Literature.” Transport Reviews 26 (2): 127–149. doi:https://doi.org/10.1080/01441640500333677.
   Web of Science ®Google Scholar
• Douglas, N., and G. Karpouzis, 2006. Estimating the Passenger Cost of Train Overcrowding, in: 29th Australasian Transport Research Forum. Gold Coast, Australia.
   Google Scholar
• Douglas, N. J., and M. Jones, 2013. Estimating Transfer Penalties and Standardised Income Values of Time by Stated Preference Survey, Australasian Transport Research Forum, ATRF 2013 - Proceedings. Brisbane, Australia.
   Google Scholar
• Godwin, A., 2012. King’s Cross St. Pancras 3D station map [WWW Document]. https://stations.aeracode.org/
   Google Scholar
• Guo, Z., and N. H. M. Wilson. 2011. “Assessing the Cost of Transfer Inconvenience in Public Transport Systems: A Case Study of the London Underground.” Transportation Research Part A: Policy and Practice 45 (2): 91–104. doi:https://doi.org/10.1016/j.tra.2010.11.002.
   Web of Science ®Google Scholar
• Hess, S., Train, K.E., Polak, J.W. 2006. “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 40 (2): 147–163. doi:https://doi.org/10.1016/j.trb.2004.10.005
   Web of Science ®Google Scholar
• Hua, W., X. Feng, C. Ding, and Z. Ruan. 2020. “Bayesian Network Modeling Analyzes of Perceived Urban Rail Transfer Time.” Transportation Letters 1–8. doi:https://doi.org/10.1080/19427867.2020.1733201.
   Google Scholar
• Jia, F., X. Jiang, H. Li, X. Yu, X. Xu, and M. Jiang. 2020. “Passenger-oriented Subway Network Capacity Calculation and Analysis Based on Simulation.” Transportation Letters 1–13. doi:https://doi.org/10.1080/19427867.2020.1741778.
   Google Scholar
• Jin, F., E. Yao, Y. Zhang, S. Liu, and X. Ma. 2017. “Metro Passengers’ Route Choice Model and Its Application considering Perceived Transfer Threshold.” PLoS One 12 (9): 1–17. doi:https://doi.org/10.1371/journal.pone.0185349.
   Web of Science ®Google Scholar
• Jung, Y. J., and J. M. Casello. 2019. “Assessment of the Transit Ridership Prediction Errors Using AVL/APC Data.” Transportation (Amsterdam). doi:https://doi.org/10.1007/s11116-019-09985-7.
   Google Scholar
• Kem, O., F. Balbo, and A. Zimmermann. 2017. “Traveler-Oriented Advanced Traveler Information System Based on Dynamic Discovery of Resources: Potentials and Challenges.” Transportation Research Procedia 22: 635–644. doi:https://doi.org/10.1016/j.trpro.2017.03.059.
   Google Scholar
• Lam, S. H., and F. Xie. 2002. “Transit Path-choice Models that Use Revealed Preference and Stated Preference Data.” Transportation Research Record: Journal of the Transportation Research Board 1799 (1): 58–65. doi:https://doi.org/10.3141/1799-08.
   Google Scholar
• Liu, X., 2013. Research on the Dynamic Flow Assignment Model Based on Train Schedule for Urban Subway Network. Chang’an University.
   Google Scholar
• McFadden, D. 2001. “Economic Choices.” American Economic Review 91 (3): 351–378. doi:https://doi.org/10.1257/aer.91.3.351.
   Web of Science ®Google Scholar
• McFadden, D., and K. Train. 2000. “Mixed MNL Models for Discrete Response.” Journal of Applied Econometrics 15 (5): 447–470. doi:https://doi.org/10.1002/1099-1255(200009/10)15:5<447::aid-jae570>3.0.CO;2-1.
   Web of Science ®Google Scholar
• Ministry of Land, Infrastructure, Transport and Tourism, J., 2012. Cost-effective Analysis of Railway Projects Manual 2012.
   Google Scholar
• Nuzzolo, A., A. Comi, U. Crisalli, and L. Rosati, 2013. An Advanced Pre-trip Planner with Personalized Information on Transit Networks with ATIS. IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC 2146–2151. The Hague, Netherlands. doi: https://doi.org/10.1109/ITSC.2013.6728546
   Google Scholar
• Nuzzolo, A., U. Crisalli, A. Comi, and L. Rosati. 2014. “An Advanced Traveller Advisory Tool Based on Individual Preferences.” Procedia - Social and Behavioral Sciences 160: 539–547. doi:https://doi.org/10.1016/j.sbspro.2014.12.167.
   Google Scholar
• Raveau, S., J. C. Muñoz, and L. de Grange. 2011. “A Topological Route Choice Model for Metro.” Transportation Research Part A: Policy and Practice 45 (2): 138–147. doi:https://doi.org/10.1016/j.tra.2010.12.004.
   Web of Science ®Google Scholar
• Raveau, S., Z. Guo, J. C. Muñoz, and N. H. M. Wilson. 2014. “A Behavioural Comparison of Route Choice on Metro Networks: Time, Transfers, Crowding, Topology and Socio-demographics.” Transportation Research Part A: Policy and Practice 66: 185–195. doi:https://doi.org/10.1016/j.tra.2014.05.010.
   Web of Science ®Google Scholar
• Shanghai Municipal Urban and Rural Construction and Transportation Commission, Shanghai City Comprehensive Transportation Planning Institute, Office of the Fourth Comprehensive, Transportation Survey of Shanghai, 2010. Report on the fourth Shanghai comprehensive travel survey. Shanghai.
   Google Scholar
• Train, K. E. 2009. Discrete Choice Methods with Simulation, Discrete Choice Methods with Simulation. Cambridge, United Kingdom: Cambridge university press. doi:https://doi.org/10.1016/S0898-1221(04)90100-9.
   Google Scholar
• Transport for London. 2013. Business Case Development Manual. London: TfL Programme Management Office. doi:https://doi.org/10.1002/9781118693643.ch16.
   Google Scholar
• Transport for London, 2017. Rolling Origin & Destination Survey (RODS) [WWW Document]. https://data.london.gov.uk/dataset/tfl-rolling-origin-and-destination-survey
   Google Scholar
• Wallis, I., A. Lawrence, and N. Douglas, 2013. Economic Appraisal of Public Transport Service Enhancements.
   Google Scholar
• Wardman, M. 2001. “A Review of British Evidence on Time and Service Quality Valuations.” Transportation Research Part E: Logistics and Transportation Review 37 (2–3): 107–28. doi:https://doi.org/10.1016/S1366-5545(00)00012-0
   Google Scholar
• Wardman, M., and J. Hine, 2000. Costs of Interchange: A Review of the Literature.
   Google Scholar
• Welch, T. F., and A. Widita. 2019. “Big Data in Public Transportation: A Review of Sources and Methods.” Transport Reviews 39 (6): 795–818. doi:https://doi.org/10.1080/01441647.2019.1616849.
   Web of Science ®Google Scholar