Individuals’ preferences for connecting transport choice at high-speed rail station have evolved during COVID-19, further resulting in new dynamics after COVID-19. Understanding the shifts in the factors influencing connecting transport choice is vital for effective passenger flow evacuation. However, the influence of most factors is heterogeneous, indicating that these factors exert varying impact under different conditions. This phenomenon presents a challenge in accurately capturing these shifts and developing precise countermeasures designed to promote specific modes of connecting transport. Therefore, this study aimed to investigate the context-dependent effects of factors exhibiting heterogeneity in order to elucidate the underlying causes of heterogeneity and to determine the specific impacts of these factors. Taking Beijing South Railway Station, China, as a case study, two cross-sectional surveys were conducted utilizing an identical questionnaire: one during the pandemic and another following its resolution. Then, an enhanced interpretable machine learning framework based on partially constrained temporal modeling approach was developed to elucidate the context-dependent effects while examining the shifts in these effects. Results show that seven factors marked during COVID-19, as well as fifteen factors marked after COVID-19, were retained by feature selection. Among these factors, paymode, carrying luggage, and distance from the station to the intended destination in Beijing emerged simultaneously during and after the COVID-19 pandemic, indicating that these particular factors emerged as important influences on connecting transport choice than others. Furthermore, it was noted that the effects of six factors demonstrated heterogeneity; specifically, one factor stood out particularly during the COVID-19, while five others were identified after the COVID-19. This suggests that in the post-pandemic era, the influence of various factors on connecting transport choice exhibits distinct characteristics across different conditions.
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