Road-rail intermodal travel recommendations based on a passenger heterogeneity profile

被引：0
|
作者：
Yang M. [1 ]
Li H. [1 ]
Ren Y. [1 ]
Zhang C. [1 ]
机构：
[1] School of Transportation, Southeast University, Nanjing
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2022年 / 62卷 / 07期
关键词：
Heterogeneity; Q-learning algorithm; Road-rail intermodal travel; Travel recommendations;
D O I：
10.16511/j.cnki.qhdxxb.2022.26.011
中图分类号：
学科分类号：
摘要：
Road-rail intermodal travel is one of the important intercity travel modes. However, an intercity travel recommendation method based on single factor ranking cannot satisfy the personalized travel demands of road-rail intermodal passengers. This study improves travel efficiency by using a profile database based on passenger historical ticketing data with the term frequency-inverse document frequency (TP-IDF) and K-means algorithms to explore the road-rail intermodal travel demand differences derived from the passenger heterogeneity. The model uses reward functions based on preference scores and sensitivity characteristics with the Q-learning reinforcement learning algorithm in a road-rail intermodal travel recommendation method based on the passenger heterogeneity profile. The method is applied to the Tianjin-Sihong route as a typical road-rail intermodal travel route from a megacity to small cities with road-rail intermodal travel schemes recommended for three types of passengers with different sensitivities. The results show that the recommended travel schemes shorten travel times by 20% and reduce travel costs by 32% while effectively meeting passenger behavior preferences, sensitivity characteristics and personal demands. © 2022, Tsinghua University Press. All right reserved.
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页码：1220 / 1227
页数：7
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