交通运输工程与信息学报2026，Vol.24Issue(2)：53-66,14.DOI:10.19961/j.cnki.1672-4747.2025.05.006
出行即服务环境下地铁多模式出行替代潜力评估
Evaluation of the substitution potential of metro-integrated multimodal travel in the MaaS environment
摘要
Abstract
[Background]An integrated mobility-as-a-service(MaaS)platform facilitates seamless metro-multimodal connections,improving accessibility and reducing reliance on private cars,there-by alleviating urban congestion.[Objective]Using ride-hailing trips as a benchmark,this study as-sesses the substitution potential of metro-integrated multimodal travel in the MaaS environment.It identifies the key factors influencing this substitution.[Method]First,based on the MaaS platform,the metro system is integrated with green travel modes such as conventional buses,shared bicycles,and ridesplitting to design ten types of metro-integrated multimodal travel substitution schemes,which are then matched to ride-hailing trips according to connection distances.Next,a comprehen-sive benefit evaluation method is proposed that incorporates economic gains,carbon-reduction bene-fits,and time costs to assess the substitution potential of metro-integrated multimodal travel.Final-ly,a CatBoost machine learning model is developed to predict the substitutability of ride-hailing trips under different conditions,and accumulated local effect(ALE)plots are used to explore the nonlinear effects of influencing factors.[Data]Empirical analysis is conducted using ride-hailing or-der data from Shanghai.[Result]The results show that approximately 56%of ride-hailing trips can be substituted by metro-integrated multimodal travel schemes,with the highest substitutability(ex-ceeding 80%)observed in central urban areas.Key factors affecting ride-hailing substitutability in-clude ride-hailing travel distance,proportion of travel delay,substitution scheme,distance of the metro-integrated multimodal trip,and built-environment characteristics around origins and destina-tions.[Conclusion]The study confirms the substantial potential of metro-integrated multimodal travel to reduce dependence on private cars in the MaaS environment.It also provides policymakers with evidence-based insights to optimize MaaS systems and increase the share of urban public trans-portation.关键词
城市交通/出行方式替代/机器学习/地铁多模式出行/出行即服务Key words
urban traffic/travel mode substitution/machine learning/metro-integrated multimodal travel/mobility as a service分类
交通工程引用本文复制引用
李文翔,刘博,袁炫渝,申锐滔,陈培焱..出行即服务环境下地铁多模式出行替代潜力评估[J].交通运输工程与信息学报,2026,24(2):53-66,14.基金项目
国家自然科学基金项目(72471149) （72471149）
教育部人文社会科学研究项目(24YJCZH147) （24YJCZH147）
上海市哲学社会科学规划青年课题项目(2023ECK003) （2023ECK003）
上海市教育委员会"人工智能促进科研范式改革赋能学科跃升计划"专项 （）
