Abstract：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.
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2022, Vol. 62 
