Arrival time valuation of commuters in urban rail transit
Abstract
Departure time choice of commuters is one of key decisions affecting the crowding of urban rail transit network during peak hours. It is influenced by arrival time value, the additional psychological pressure caused by in-vehicle crowding, and time uncertainty. This paper aims at investigating how commuters in urban rail transit value their arrival time at work/school. Three valuation frameworks are proposed based on the reference point approach of prospect theory. Non-linear value functions with different reference point alternatives are estimated using data from a survey and stated choice study of users of Shanghai Metro system. Results show that schedule delay with work/school start time as the only reference point cannot properly reflect the arrival time valuation of urban rail transit commuters. Instead, the valuation framework with preferred arrival time as a reference point fits best, which hits as much as 85.64% of the cases. The asymmetrical response to early-side and late-side arrivals is identified. The findings of this study provide an essential basis for the development of departure time choice model.
Keyword : urban rail transit, commuter, departure time choice, arrival time value, reference point, valuation framework
How to Cite
Cheng, Y., Ye, X., & Wang, Z. (2019). Arrival time valuation of commuters in urban rail transit. Transport, 34(3), 383-393. https://doi.org/10.3846/transport.2019.10356
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chang, G.-L.; Mahmassani, H. S. 1989. The dynamics of commuting decision behaviour in urban transportation networks, in Travel Behaviour Research: 5th International Conference on Travel Behaviour, October 1987, Aix-en-Provence, France, 15–26.
De Palma, A.; Ben-Akiva, M.; Lefevre, C.; Litinas, N. 1983. Stochastic equilibrium model of peak period traffic congestion, Transportation Science 17(4): 430–453. https://doi.org/10.1287/trsc.17.4.430
De Palma, A.; Kilani, M.; Proost, S. 2015. Discomfort in mass transit and its implication for scheduling and pricing, Transportation Research Part B: Methodological 71: 1–18. https://doi.org/10.1016/j.trb.2014.10.001
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Feng, J.; Mao, B.; Chen, Z.; Bai, Y.; Li, M. 2013. A Departure time choice for morning commute considering train capacity of a rail transit line, Advances in Mechanical Engineering 5: 582703. https://doi.org/10.1155/2013/582703
Han, L.; Ukkusuri, S.; Doan, K. 2011. Complementarity formulations for the cell transmission model based dynamic user equilibrium with departure time choice, elastic demand and user heterogeneity, Transportation Research Part B: Methodological 45(10): 1749–1767. https://doi.org/10.1016/j.trb.2011.07.007
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Hendrickson, C.; Plank, E. 1984. The flexibility of departure times for work trips, Transportation Research Part A: General 18(1): 25–36. https://doi.org/10.1016/0191-2607(84)90091-8
Huang, H.-J.; Lam, W. H. K. 2002. Modeling and solving the dynamic user equilibrium route and departure time choice problem in network with queues, Transportation Research Part B: Methodological 36(3): 253–273. https://doi.org/10.1016/S0191-2615(00)00049-7
Huang, Y. 2010. Research on Urban Rail Transit Passenger Flow Assignment Model and Algorithm. MSc Thesis. Beijing Jiao-tong University, China, 53 p. (in Chinese).
Ieda, H.; Tsuchiya, K.; Phan, L. B.; Okamura, T. 2002. Development of the commuter demand concentration model based on a time-space network scheme, Journal of Japan Society of Civil Engineers (702): 65–79. https://doi.org/10.2208/jscej.2002.702_65 (in Japanese).
Iwakura, S.; Harada, C. 2005. A model system of departure time choice for commuter trips by metropolitan railway, Transport Policy Studies’ Review 8: 4–15.
Iwakura, S.; Harada, C.; Suzuki, S. 2003. Comparative analysis of choice set for commuting time of day choice model in urban railway networks, Infrastructure Planning Review 20(3): 485–492. https://doi.org/10.2208/journalip.20.485
Jou, R.-C.; Kitamura, R. 2002. Commuter departure time choice: a reference-point approach, in 9th Meeting of the Euro Working Group on Transportation, 10–13 June 2002, Bari, Italy, 149–155. Available from Internet: http://www.iasi.cnr.it/ewgt/13conference/26_jou.pdf
Jou, R.-C.; Kitamura, R.; Weng, M.-C.; Chen, C.-C. 2008. Dynamic commuter departure time choice under uncertainty, Transportation Research Part A: Policy and Practice 42(5): 774–783. https://doi.org/10.1016/j.tra.2008.01.017
Kahneman, D.; Tversky, A. 1979. Prospect theory: an analysis of decision under risk, Econometrica 47(2): 263–291. https://doi.org/10.2307/1914185
Kristoffersson, I. 2007. Implementation of model for departure time choice, in TRISTAN VI – Sixth Triennial Symposium on Transportation Analysis, 10–15 June 2007, Phuket Island, Thailand, 1–19.
Liu, J. 2012. Transfer-based Urban Rail Transit Flow Distribution Modeling and Empirical Study. Doctoral Dissertation. Beijing Jiaotong University, China, 119 p. (in Chinese).
Liu, X. 2013. Research on the Dynamic Flow Assignment Model Based on Train Schedule for Urban Subway Network. Doctoral Dissertation. Chang’an University, China, 135 p. (in Chinese).
Mahmassani, H. S.; Chang, G.-L. 1986. Experiments with departure time choice dynamics of urban commuters, Transportation Research Part B: Methodological 20(4): 297–320. https://doi.org/10.1016/0191-2615(86)90045-7
Noland, R. B.; Small, K. A. 1995. Travel-time uncertainty, departure time choice, and the cost of morning commutes, Transportation Research Record 1493: 150–158.
Peer, S.; Knockaert, J.; Verhoef, E. T. 2016. Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment, Transportation Research Part B: Methodological 83: 314–333. https://doi.org/10.1016/j.trb.2015.11.017
Poon, M. H.; Wong, S. C.; Tong, C. O. 2004. A dynamic schedule-based model for congested transit networks, Transportation Research Part B: Methodological 38(4): 343–368. https://doi.org/10.1016/S0191-2615(03)00026-2
Ran, B.; Hall, R. W.; Boyce, D. E. 1996. A link-based variational inequality model for dynamic departure time/route choice, Transportation Research Part B: Methodological 30(1): 31–46. https://doi.org/10.1016/0191-2615(95)00010-0
Senbil, M.; Kitamura, R. 2004. Reference points in commuter departure time choice: a prospect theoretic test of alternative decision frames, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 8(1): 19–31. https://doi.org/10.1080/15472450490437726
Simon, H. A. 1955. A behavioral model of rational choice, The Quarterly Journal of Economics 69(1): 99–118. https://doi.org/10.2307/1884852
Small, K. A. 1982. The scheduling of consumer activities: work trips, The American Economic Review 72(3): 467–479.
Soyama, Y.; Kaneko, Y.; Kato, H. 2010. Departure time choice under the condition of daily service delay in urban railway, Proceedings of Infrastructure Planning 41: 1–4. (in Japanese).
Tian, Q.; Huang, H. 2004. An equilibrium ride model for subway passengers with arrival early penalty, Journal of Transportation Systems Engineering and Information Technology 4(4): 108–112. (in Chinese).
Tian, Q.; Huang, H.-J.; Yang, H. 2007. Equilibrium properties of the morning peak-period commuting in a many-to-one mass transit system, Transportation Research Part B: Methodological 41(6): 616–631. https://doi.org/10.1016/j.trb.2006.10.003
Van de Kaa, E. J. 2010. Applicability of an extended prospect theory to travel behaviour research: a meta‐analysis, Transport Reviews 30(6): 771–804. https://doi.org/10.1080/01441647.2010.486907
Vickrey, W. S. 1969. Congestion theory and transport investment, The American Economic Review 59(2): 251–260.
Wu, W.; Huang, H. 2009. Model of subway commuters’ departure time choice with in-carriage congestion and arrival early/late penalty, Journal of Transportation Systems Engineering and Information Technology 9(1): 128–132. (in Chinese).
Wu, X.-Y.; Liu, C.-Q. 2004. Traffic equilibrium assignment model specially for urban railway network, Journal of Tongji University (Natural Science) 32(9): 1158–1162. (in Chinese).
Yang, D. 2013. Research on Schedule-based Rail Transit Passenger Flow Assignment. MSc Thesis. Beijing Jiaotong University, China, 72 p. (in Chinese).
Yang, G.; Liu, X. 2018. A commuter departure-time model based on cumulative prospect theory, Mathematical Methods of Operations Research 87(2): 285–307. https://doi.org/10.1007/s00186-017-0619-8
Yang, H.; Meng, Q. 1998. Departure time, route choice and congestion toll in a queuing network with elastic demand, Transportation Research Part B: Methodological 32(4): 247–260. https://doi.org/10.1016/S0191-2615(97)00041-6
Yang, H.; Tang, Y. 2018. Managing rail transit peak-hour congestion with a fare-reward scheme, Transportation Research Part B: Methodological 110: 122–136. https://doi.org/10.1016/j.trb.2018.02.005
Arnott, R.; De Palma, A.; Lindsey, R. 1990. Departure time and route choice for the morning commute, Transportation Research Part B: Methodological 24(3): 209–228. https://doi.org/10.1016/0191-2615(90)90018-T
Ben-Akiva, M. E.; Lerman, S. R. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. 412 p.
Chang, G.-L.; Mahmassani, H. S. 1989. The dynamics of commuting decision behaviour in urban transportation networks, in Travel Behaviour Research: 5th International Conference on Travel Behaviour, October 1987, Aix-en-Provence, France, 15–26.
De Palma, A.; Ben-Akiva, M.; Lefevre, C.; Litinas, N. 1983. Stochastic equilibrium model of peak period traffic congestion, Transportation Science 17(4): 430–453. https://doi.org/10.1287/trsc.17.4.430
De Palma, A.; Kilani, M.; Proost, S. 2015. Discomfort in mass transit and its implication for scheduling and pricing, Transportation Research Part B: Methodological 71: 1–18. https://doi.org/10.1016/j.trb.2014.10.001
De Palma, A.; Lindsey, R.; Monchambert, G. 2017. The economics of crowding in rail transit, Journal of Urban Economics 101: 106–122. https://doi.org/10.1016/j.jue.2017.06.003
Feng, J.; Mao, B.; Chen, Z.; Bai, Y.; Li, M. 2013. A Departure time choice for morning commute considering train capacity of a rail transit line, Advances in Mechanical Engineering 5: 582703. https://doi.org/10.1155/2013/582703
Han, L.; Ukkusuri, S.; Doan, K. 2011. Complementarity formulations for the cell transmission model based dynamic user equilibrium with departure time choice, elastic demand and user heterogeneity, Transportation Research Part B: Methodological 45(10): 1749–1767. https://doi.org/10.1016/j.trb.2011.07.007
Harada, C.; Iwakura, S.; Morichi, S. 2002. Analysis and modeling of commuters’ departure time in urban railway network, Proceedings of Infrastructure Planning 26: 1–4. (in Japanese).
Hendrickson, C.; Plank, E. 1984. The flexibility of departure times for work trips, Transportation Research Part A: General 18(1): 25–36. https://doi.org/10.1016/0191-2607(84)90091-8
Huang, H.-J.; Lam, W. H. K. 2002. Modeling and solving the dynamic user equilibrium route and departure time choice problem in network with queues, Transportation Research Part B: Methodological 36(3): 253–273. https://doi.org/10.1016/S0191-2615(00)00049-7
Huang, Y. 2010. Research on Urban Rail Transit Passenger Flow Assignment Model and Algorithm. MSc Thesis. Beijing Jiao-tong University, China, 53 p. (in Chinese).
Ieda, H.; Tsuchiya, K.; Phan, L. B.; Okamura, T. 2002. Development of the commuter demand concentration model based on a time-space network scheme, Journal of Japan Society of Civil Engineers (702): 65–79. https://doi.org/10.2208/jscej.2002.702_65 (in Japanese).
Iwakura, S.; Harada, C. 2005. A model system of departure time choice for commuter trips by metropolitan railway, Transport Policy Studies’ Review 8: 4–15.
Iwakura, S.; Harada, C.; Suzuki, S. 2003. Comparative analysis of choice set for commuting time of day choice model in urban railway networks, Infrastructure Planning Review 20(3): 485–492. https://doi.org/10.2208/journalip.20.485
Jou, R.-C.; Kitamura, R. 2002. Commuter departure time choice: a reference-point approach, in 9th Meeting of the Euro Working Group on Transportation, 10–13 June 2002, Bari, Italy, 149–155. Available from Internet: http://www.iasi.cnr.it/ewgt/13conference/26_jou.pdf
Jou, R.-C.; Kitamura, R.; Weng, M.-C.; Chen, C.-C. 2008. Dynamic commuter departure time choice under uncertainty, Transportation Research Part A: Policy and Practice 42(5): 774–783. https://doi.org/10.1016/j.tra.2008.01.017
Kahneman, D.; Tversky, A. 1979. Prospect theory: an analysis of decision under risk, Econometrica 47(2): 263–291. https://doi.org/10.2307/1914185
Kristoffersson, I. 2007. Implementation of model for departure time choice, in TRISTAN VI – Sixth Triennial Symposium on Transportation Analysis, 10–15 June 2007, Phuket Island, Thailand, 1–19.
Liu, J. 2012. Transfer-based Urban Rail Transit Flow Distribution Modeling and Empirical Study. Doctoral Dissertation. Beijing Jiaotong University, China, 119 p. (in Chinese).
Liu, X. 2013. Research on the Dynamic Flow Assignment Model Based on Train Schedule for Urban Subway Network. Doctoral Dissertation. Chang’an University, China, 135 p. (in Chinese).
Mahmassani, H. S.; Chang, G.-L. 1986. Experiments with departure time choice dynamics of urban commuters, Transportation Research Part B: Methodological 20(4): 297–320. https://doi.org/10.1016/0191-2615(86)90045-7
Noland, R. B.; Small, K. A. 1995. Travel-time uncertainty, departure time choice, and the cost of morning commutes, Transportation Research Record 1493: 150–158.
Peer, S.; Knockaert, J.; Verhoef, E. T. 2016. Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment, Transportation Research Part B: Methodological 83: 314–333. https://doi.org/10.1016/j.trb.2015.11.017
Poon, M. H.; Wong, S. C.; Tong, C. O. 2004. A dynamic schedule-based model for congested transit networks, Transportation Research Part B: Methodological 38(4): 343–368. https://doi.org/10.1016/S0191-2615(03)00026-2
Ran, B.; Hall, R. W.; Boyce, D. E. 1996. A link-based variational inequality model for dynamic departure time/route choice, Transportation Research Part B: Methodological 30(1): 31–46. https://doi.org/10.1016/0191-2615(95)00010-0
Senbil, M.; Kitamura, R. 2004. Reference points in commuter departure time choice: a prospect theoretic test of alternative decision frames, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 8(1): 19–31. https://doi.org/10.1080/15472450490437726
Simon, H. A. 1955. A behavioral model of rational choice, The Quarterly Journal of Economics 69(1): 99–118. https://doi.org/10.2307/1884852
Small, K. A. 1982. The scheduling of consumer activities: work trips, The American Economic Review 72(3): 467–479.
Soyama, Y.; Kaneko, Y.; Kato, H. 2010. Departure time choice under the condition of daily service delay in urban railway, Proceedings of Infrastructure Planning 41: 1–4. (in Japanese).
Tian, Q.; Huang, H. 2004. An equilibrium ride model for subway passengers with arrival early penalty, Journal of Transportation Systems Engineering and Information Technology 4(4): 108–112. (in Chinese).
Tian, Q.; Huang, H.-J.; Yang, H. 2007. Equilibrium properties of the morning peak-period commuting in a many-to-one mass transit system, Transportation Research Part B: Methodological 41(6): 616–631. https://doi.org/10.1016/j.trb.2006.10.003
Van de Kaa, E. J. 2010. Applicability of an extended prospect theory to travel behaviour research: a meta‐analysis, Transport Reviews 30(6): 771–804. https://doi.org/10.1080/01441647.2010.486907
Vickrey, W. S. 1969. Congestion theory and transport investment, The American Economic Review 59(2): 251–260.
Wu, W.; Huang, H. 2009. Model of subway commuters’ departure time choice with in-carriage congestion and arrival early/late penalty, Journal of Transportation Systems Engineering and Information Technology 9(1): 128–132. (in Chinese).
Wu, X.-Y.; Liu, C.-Q. 2004. Traffic equilibrium assignment model specially for urban railway network, Journal of Tongji University (Natural Science) 32(9): 1158–1162. (in Chinese).
Yang, D. 2013. Research on Schedule-based Rail Transit Passenger Flow Assignment. MSc Thesis. Beijing Jiaotong University, China, 72 p. (in Chinese).
Yang, G.; Liu, X. 2018. A commuter departure-time model based on cumulative prospect theory, Mathematical Methods of Operations Research 87(2): 285–307. https://doi.org/10.1007/s00186-017-0619-8
Yang, H.; Meng, Q. 1998. Departure time, route choice and congestion toll in a queuing network with elastic demand, Transportation Research Part B: Methodological 32(4): 247–260. https://doi.org/10.1016/S0191-2615(97)00041-6
Yang, H.; Tang, Y. 2018. Managing rail transit peak-hour congestion with a fare-reward scheme, Transportation Research Part B: Methodological 110: 122–136. https://doi.org/10.1016/j.trb.2018.02.005