As a crucial complement to conventional public transportation, flexible bus can provide demandresponsive services tailored to specific groups, and it has been successfully implemented and proven effective in foreign countries. However, whether it can be applied to connect passengers at comprehensive transport hubs and alleviate the increasing pressure of passenger flows at these hubs, which has become a prominent issue in the field of urban public transportation in China, warrants further investigation. To address this, this research established a flexible bus dispatching optimization method for comprehensive hub connection. Based on the characteristics of data sharing and flexible response of MaaS system, a MaaS-based flexible connecting bus dispatching service process was constructed. Considering both passengers’punctuality requirements and the cost considerations of public transit operators, the study developed a multi-objective optimization model by incorporating constraints like time windows, vehicle capacity, and station services. The multiple objective model was transformed into a single objective model by unifying the solution direction, normalization and empowerment. The differential evolution algorithm was designed based on the ideas of encoding, decoding and maximum heap, and the model was verified by taking the railway hub area of Nanjing South Railway Station as a case. Relying on smart card data from selected bus routes in the vicinity of Nanjing South Station in May 2021, the study analyzed the spatial distribution characteristics of passenger travel demands at the hub and established predefined demand sites and passenger travel needs. The model algorithm was iteratively optimized, resulting in a fitness value of 0.9212 and an average passenger satisfaction of 89.77%. The algorithm converges within 50 iterations, thus verifying the feasibility and effectiveness of the model and algorithm. Sensitivity analysis demonstrates that the model and algorithm remain highly applicable even when passenger demand scales change.

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.