ABSTRACT Clarifying passengers’ travel behaviour under dynamic pricing strategies for high‐speed railway (HSR) can provide theoretical foundations and practical insights for railway operators to develop personalized and differentiated marketing strategies and pricing schemes. Firstly, this paper extends the theory of planned behaviour (TPB) by introducing ‘travel habits’ and ‘perceived travel fairness’ as latent variables, constructing a psychological decision‐making framework that accounts for passenger heterogeneity under HSR dynamic pricing strategies. Secondly, employing revealed preference (RP) and stated preference (SP) survey methods combined with D‐optimal experimental design, we designed a realistic experimental scenario to accurately capture passenger choice behaviour under dynamic pricing conditions. Then, by integrating the multiple indicators multiple causes (MIMIC) model and latent class model (LCM), we developed a hybrid model to analyse passenger heterogeneity from both latent variable and latent class perspectives, uncovering behavioural patterns and segmenting passenger groups. Finally, empirical analysis based on survey data revealed that the hybrid model classified passengers into four distinct segments: Time‐sensitive (24.37%), cost‐sensitive (20.03%), scenario‐dependent (41.27%) and experience‐oriented (14.33%). The model systematically elucidates the behaviour habits and preference characteristics of each passenger segment, providing robust support for the sustainable reform and optimization of HSR dynamic pricing strategies.
Guo et al. (Thu,) studied this question.