Passenger choice between demand-responsive train and pre-scheduled train in high-speed railway: A stated preference study

Abstract

Demand-responsive transportation has been introduced in many cities around the world. However, whether it is applicable in the railway is still questionable, an exploration of passenger choice behavior between demand-responsive trains and pre-scheduled trains is pivotal in addressing this issue. To delve into passengers’ choice preferences when facing demand-responsive trains and to dissect the feasibility of implementing demand-responsive service in high-speed railways, the stated preference survey method is employed to investigate travel intention of passengers. Based on the survey data obtained in China, the heterogeneity of passengers is analyzed from three aspects: personal socio-economic characteristics, travel characteristics, and travel mode choice. Considering the situation that demand-responsive train cannot operate, the risk attributes are considered. To bolster the appeal of demand-responsive trains, personalized service product attributes are added. Mixed Logit mode, which takes into account the heterogeneous travel choice behavior of passengers, is developed, and Maximum Likelihood Estimation and the Monte Carlo method are used to calibrate model parameters. The willingness to pay in terms of different factors of passengers is determined. The results indicate that early arrival deviation time, late arrival deviation time, demand response time, and success rate of ticket purchase remarkable influence passengers’ decision regarding demand-responsive train, with only the success rate of ticket purchase positively impacting train choice. Moreover, the significant difference in train ticket price is observed solely in the self-funded long distance scenario, while demand-responsive trains are found to be particularly appealing in self-funded short distance scenario. Through the Willingness To Pay (WTP) analysis, it is discovered that by shortening demand response time, enhancing the success rate of ticket purchase, and minimizing the deviation times of early arrival and late arrival of trains, the attractiveness of the demand-responsive train to passengers under three travel scenarios can be augmented. This study provides profound insights into the possibility of railway enterprises operating demand-responsive trains.

Keywords

High-speed railway / Demand-responsive train / Stated preference / Mixed Logit model / Willingness to pay analysis

CRediT authorship contribution statement

Tao Li: Writing - review & editing, Writing - original draft, Methodology, Conceptualization. Dewei Li: Writing - review & editing, Methodology, Conceptualization. Yongsheng Wang: Software, Investigation. Han Gao: Software, Investigation. Jialun Ma: Visualization. Haotian Ji: Visualization

Data availability

Data will be made available on request.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research is supported by the National Natural Science Foundation of China (No. 72471023, 71971019), and the Fundamental Research Funds for the Central Universities (No. 2024QYBS025).

Appendix. List of frequently used abbreviations

DRT: Demand Responsive Transit

WTP: Willingness To Pay

RP: Revealed Preference

SP: Stated Preference

ML: Mixed Logit

MNL: Multinomial Logit

SFSD: Self-Funded Short Distance

PFLD: Publicly-Funded long Distance

SFLD: Self-funded Long Distance

DT: Departure Time

STO: Success rate of Train Operation

DRT-PS: Demand-Responsive Train with Personalized Service

TP: Ticket Price

TRT: Train Running Time

DR-T: Demand Response Time

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