Preference heterogeneity analysis on train choice behaviour of high-speed railway passengers: A case study in China

Pengfang Chen, Xiaoqiang Zhang and Dongsheng Gao

Transportation Research Part A: Policy and Practice, 2024, vol. 188, issue C

Abstract: This study aims at revealing and quantifying preference heterogeneity on train choice behaviour of high-speed railway (HSR) passengers to better-improving the quality of passenger service. The analysis is performed using characteristic data and 3,355 unlabeled stated preference (SP) experiments data from 671 respondents on Nanning-Guangzhou passenger corridor in China. Utilizing Expectation-Maximization (EM) iterations, the Gaussian Mixture Model (GMM) identified four heterogeneous travel groups among HSR passengers based on SED and travel characteristics variables, named as Price-Sensitive (PS), General Business (GB), Peak-hours Enthusiastic (PE), and Leisure Experience Focuser (LEF). The Random Parameter Logit (RPL) estimation revealed the preference heterogeneity of the four groups regarding basic attributes (ticket price, travel time, departure date), additional service attributes (catering reservations-to-seats, silent carriages, large luggage check-in), and departure time (peak hours, flat hours, valley hours) of high-speed trains. The willingness to pay of different groups for these attributes also showed distinct differences. In addition, the two-step approach GMM-RPL with the integration of machine learning, demonstrating the enhanced interpretability and capability for latent heterogeneity identification compared to Latent Class Choice Model (LCCM). This study can provide decision support for railway operators in designing competitive train products and implementing differential pricing strategy to improve the quality of HSR service and increase the operational revenue.

Keywords: High-speed trains; Choice preference heterogeneity; Unlabelled stated preference experiments; Gaussian Mixture Model; Random Parameter Logit (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tra.2024.104198

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