Air-rail intermodal services (ARISs) represent a highly promising multimodal solution within the transportation sector. Nonetheless, various uncertainties and challenges persist across multiple dimensions of air-rail interline travel, with discrepancies in passenger perceptions being a notable aspect. In an effort to pinpoint the pivotal factors contributing to these disparities among distinct passenger profiles, this study employs the Structural Equation Modeling-Multiple Indicator Multiple Cause-Artificial Neural Network (SEM-MIMIC-ANN) methodology. This approach explores the impact of numerous attributes on passenger perceptions in the context of air-rail intermodal travel, leveraging questionnaire data gathered from Shijiazhuang multimodal passengers. Furthermore, the study utilizes the Classification and Regression Tree (CART) decision tree algorithm to categorize actual passengers into distinct characteristic groups. Subsequently, the perception levels of these diverse passenger groups are quantified through the calculation of comprehensive evaluation function values. In conclusion, taking into account the real-world conditions of air-rail interline travel, this research formulates a tailored service strategy aimed at enhancing the overall passenger experience.

中文翻译：

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空铁联运旅客感知异质性分析及差异化服务策略


空铁联运服务（ARIS）代表了交通运输领域极具前景的多式联运解决方案。尽管如此，空铁联程旅行的多个层面仍然存在各种不确定性和挑战，其中乘客认知的差异是一个值得注意的方面。为了查明导致不同乘客资料之间差异的关键因素，本研究采用了结构方程模型-多指标多原因-人工神经网络 (SEM-MIMIC-ANN) 方法。该方法利用从石家庄多式联运乘客收集的问卷数据，探讨了空铁联运旅行中众多属性对乘客感知的影响。此外，该研究利用分类回归树（CART）决策树算法将实际乘客分类为不同的特征组。随后，通过综合评价函数值的计算，量化这些不同乘客群体的感知水平。总之，考虑到空铁联运旅行的现实情况，本研究制定了量身定制的服务策略，旨在提升整体旅客体验。