Bayesian network modeling analyzes of perceived urban rail transfer time

Weixin HuaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, P.R. ChinaView further author information

Xuesong FengSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, P.R. ChinaCorrespondence[email protected]
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Chuanchen DingSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, P.R. ChinaView further author information

Zejing RuanSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing, P.R. ChinaView further author information

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Yan Cheng, Xiafei Ye & Taku Fujiyama. (2022) How does interchange affect passengers’ route choices in urban rail transit? – a case study of the Shanghai Metro. Transportation Letters 14:4, pages 416-426.
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Articles from other publishers (2)

Yalong Yuan, Min Yang, Tao Feng, Yafeng Ma, Yifeng Ren & Xinpei Ruan. (2022) Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data. Transportation Research Part A: Policy and Practice 165, pages 533-552.
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Gunwoo YongGhang Lee. (2022) Trends, Topics, Leaders, Influential Studies, and Future Challenges of Machine Learning Studies in the Rail Industry. Journal of Infrastructure Systems 28:2.
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Bayesian network modeling analyzes of perceived urban rail transfer time

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Bayesian network modeling analyzes of perceived urban rail transfer time

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