Multi-route operation is a key feature of the networked operation system in China's urban rail transit. Investigating passengers' travel choice behavior under such conditions is crucial for understanding passenger travel patterns of and meeting diverse travel demands. Based on minimum-regret theory, this study incorporates the passenger heterogeneity in the perception of route attributes to construct a multi-scale hybrid utility-regret model that integrates utility maximization and regret minimization mechanisms. This model addresses the decision-making deviation between predicted passengers' travel behavior by traditional models and their actual travel behavior, a discrepancy arising from the passengers' route familiarity. By integrating tolerance thresholds and decision inertia, we propose a modeling approach for multi-route travel choice and estimate model parameters and validate performance using stated preference (SP) survey data from a representative case. The results show that passengers exhibit a tolerance threshold of 6.98 minutes for travel time attribute. Compared with the benchmark model, the proposed model demonstrates superior performance in terms of log-likelihood, Bayesian information criterion (BIC), and hit rate, thereby confirming its stronger data-fitting capability. Furthermore, the analysis of willingness-to-pay reveals that passengers are willing to accept additional time costs for improved service quality, confirming the model's effectiveness and practical applicability.
GONG et al. (Thu,) studied this question.
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