The mixed itinerary-size weibit (MISW) model was recently developed for predicting passengers’ itinerary-choice behaviors in a schedule-based railway network. It considers passengers’ heterogeneous perceptions and relaxes the independently and identically distributed assumptions of random utility models. However, this model has not been verified using real-world data. Moreover, it is assumed that passengers hold a negative perception of overlapping, but this assumption may not be suitable for all situations. Thus, this study proposes a scaled MISW model which includes a scale parameter to address this issue. We collected passenger ticket-booking data from the South China High-Speed Railway network and conducted an empirical analysis in which we compared the performances of the scaled MISW model and other models (i.e. the multinomial logit, multinomial weibit, and MISW models). According to the results, the scaled MISW model outperformed the other models in describing passengers’ choice behaviors in the railway network.

最近开发了混合行程大小 weibit (MISW) 模型，用于预测基于时刻表的铁路网络中乘客的行程选择行为。它考虑了乘客的异质感知，放宽了随机效用模型的独立同分布假设。但是，该模型尚未使用真实数据进行验证。此外，假设乘客对重叠持有负面看法，但这种假设可能不适用于所有情况。因此，本研究提出了一个规模化的 MISW 模型，其中包括一个规模参数来解决这个问题。我们收集了华南高铁网络的客票预订数据，并进行了实证分析，比较了规模化 MISW 模型和其他模型（即多项 logit、多项式weibit和MISW模型）。根据结果​​，规模化的 MISW 模型在描述乘客在铁路网络中的选择行为方面优于其他模型。