Passenger choice behaviour of buying tickets has a great impact on the high-speed rail (HSR) revenue management. It is very critical to find out the sensitive factors that prevent passengers with high willingness to pay for a ticket from buying low-price tickets. The literature on passenger choice behaviour mainly focuses on travel mode choice, choice between a conventional train and a high-speed train and choice among high-speed trains. To extend the literature and serve revenue management, this paper investigates passenger choice behaviour of buying high-speed railway tickets. The data were collected by the stated preference (SP) survey based on Beijing-Hohhot high-speed railway. The conditional logit model was established to analyse influencing factors for business travel and non-business travel. The results show that: business passengers have the higher inherent preference for full-price tickets, while non-business passengers have the higher inherent preference for discount tickets; the number of days booked in advance and frequent passenger points have a significant impact on the ticket choice of business travellers, but not on non-business travellers; passengers are unwilling to buy tickets that depart after 16:00 for non-business travel; factors have different effects on the passengers' choice in business travel and non-business travel. The results can provide parameters for revenue management models and references for the ticket-product design.
Strauss AK, Klein R, Steinhardt C. A review of choice-based revenue management: Theory and methods. European Journal of Operational Research. 2018;271(2): 375-387. doi: 10.1016/j.ejor.2018.01.011.
Rui H, Wu Q. Medium and long-distance travel mode decision between high-speed rail and civil aviation. China Journal of Highway and Transport. 2016;29(3): 134-141. doi: 10.19721/j.cnki.1001-7372.2016.03.017.
Ye Y, Han M, Chen JJ. Intercity passenger travel mode choice behavior based on trip chain. Journal of Tongji University (Nature science). 2018;46(09): 77-83. doi: 10.11908/j.issn.0253-374x.2018.09.011.
Cao W, et al. Investigating passengers choice behavior of intercity rails with large-scale ticketing data. System Engineering Theory and Practice. 2020;40(4): 989-1000. doi: 10.12011/1000-6788-2018-2198-12.
Zhao P, Zhai R, Song W. Passengers choice behavior of high-speed railway considering individual heterogeneity. Journal of Beijing Jiaotong University. 2019;43(02): 121-127. doi: 10.11860/j.issn.1673-0291.2017.06.008.
Sun Y, et al. Analyzing high speed rail passengers’ train choices based on new online booking data in China. Transportation Research Part C: Emerging Technologies. 2018;97: 96-113. doi: 10.1016/j.trc.2018.10.015.
Hetrakul P, Cirillo C. Accommodating taste heterogeneity in railway passenger choice models based on internet booking data. Journal of Choice Modelling. 2013;6: 1-16. doi: 10.1016/j.jocm.2013.04.003.
Yan Z, Li X, Zhang Q, Han B. Seat allocation model for high-speed railway passenger transportation based on flexible train composition. Computers and Industrial Engineering. 2020;142. doi: 10.1016/j.cie.2020.106383.
Newman JP, et al. Estimation of choice-based models using sales data from a single firm. Manufacturing & Service Operations Management. 2014;16(2): 184-197. doi: 10.1287/msom.2014.0475.
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. 2018;30(5): 549-561. doi: 10.7307/ptt.v30i5.2623.
Lai, X, Li J, Li Z. A subpath-based logit model to capture the correlation of routes. Promet – Traffic&Transportation. 2016;28(3): 225-234. doi: 10.7307/ptt.v28i3.1808.
Ramírez HG, et al. Travel time and bounded rationality in travellers' route choice behaviour: A computer route choice experiment. Travel Behaviour and Society. 2021;22: 59-83. doi: 10.1016/j.tbs.2020.06.011.
Freitas LM, Becker H, Zimmermann M, Axhausen KW. Modelling intermodal travel in Switzerland: A recursive logit approach. Transportation Research Part A: Policy & Practice. 2019;119: 200-213. doi: 10.1016/j.tra.2018.11.009.
Luan X, Cheng L, Song Y, Zhao J. Better understanding the choice of travel mode by urban residents: New insights from the catchment areas of rail transit stations. Sustainable Cities and Society. 2020;53: 10196. doi: 10.1016/j.scs.2019.101968.
Weis C, et al. Surveying and analysing mode and route choices in Switzerland 2010–2015. Travel Behaviour and Society. 2021;22: 10-21. doi: 10.1016/j.tbs.2020.08.001.
Jian M, Shi J, Liu Y. Dependence of the future elderly on private cars: A case study in Beijing. Promet – Traffic&Transportation. 2018;30(1): 45-55. doi: 10.7307/ptt.v30i1.2364.
Cheng L, et al. Applying a random forest method approach to model travel mode choice behaviour. Travel Behaviour and Society. 2019;14: 1-10. doi: 10.1016/j.tbs.2018.09.002.
Cheng L, et al. Applying an ensemble-based model to travel choice behavior in travel demand forecasting under uncertainties. Transportation Letters. 2020;12(6): 375-385. doi: 10.1080/19427867.2019.1603188.
Li Z, Hensher D. Understanding risky choice behaviour with travel time variability: A review of recent empirical contributions of alternative behavioural theories. Transportation Letters. 2020;12(8): 580-590. doi: 10.1080/19427867.2019.1662562.
Zhao AX, Yan B, Yu A, van Hentenryck P. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society. 2020;20: 22-35. doi: 10.1016/j.tbs.2020.02.003.
Zhou H, et al. Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia. Transportation Research Part A: Policy and Practice. 2020;137: 187-205. doi: 10.1016/j.tra.2020.04.020.
Hess S, Spitz G, Bradley M, Coogan M. Analysis of mode choice for intercity travel: Application of a hybrid choice model to two distinct US corridors. Transportation Research Part A: Policy and Practice. 2018;116: 547-567. doi: 10.1016/j.tra.2018.05.019.
Li X, Tang J, Hu X, Wang W. Assessing intercity multimodal choice behavior in a touristy city: A factor analysis. Journal of Transport Geography. 2020;86: 1-13. doi: 10.1016/j.jtrangeo.2020.102776.
Allard RF, Moura F. Effect of transport transfer quality on intercity passenger mode choice. Transportation Research Part A: Policy and Practice. 2018;109: 89-107. doi: 10.1016/j.tra.2018.01.018.
Li H, Wang K, Yu K, Zhang A. Are conventional train passengers underserved after entry of high-speed rail? -Evidence from Chinese intercity markets. Transport Policy. 2020;95: 1-9. doi: 10.1016/j.tranpol.2020.05.017.
Ren X, et al. Impact of high-speed rail on social equity in China: Evidence from a mode choice survey. Transportation Research Part A: Policy and Practice. 2020;138: 422-441. doi: 10.1016/j.tra.2020.05.018.
Losada-Rojas LL, Gkartzonikas C, Pyrialakou VD, Gkritza K. Exploring intercity passengers' attitudes and loyalty to intercity passenger rail: Evidence from an on-board survey. Transport Policy. 2019;73: 71-83. doi: 10.1016/j.tranpol.2018.10.011.
Daly A, et al. Using ordered attitudinal indicators in a latent variable choice model: A study of the impact of security on rail travel behaviour. Transportation. 2012;39(2): 267-297. doi: 10.1007/s11116-011-9351-z.
Yang CW, Tsai MC, Chang CC. Investigating the joint choice behavior of intercity transport mode and high-speed rail cabin with a strategy map. Journal of Advanced Transportation. 2014;49: 297-308. doi:10.1002/atr.1264.
Yang X, et al. Car ownership policies in China: Preferences of residents and influence on the choice of electric cars. Transport Policy. 2017;58(8), 62-71. doi: 10.1016/j.tranpol.2017.04.010.
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