Improving Synchronization in High-Speed Railway and Air Intermodality: Integrated Train Timetable Rescheduling and Passenger Flow Forecasting

With the aim of synchronizing high-speed railway (HSR) and aviation services to adapt intermodality traffic needs, this paper is concerned with HSR-Air timetable coordination (HATC) problem. This problem is solved by rescheduling the HSR timetable with the goal of attracting the maximum HSR-Air passenger flow, and at the same time, considering the minimum adjustments to the initial HSR timetable. The HATC problem is an integration of train timetable rescheduling and HSR-air passenger flow predicting problem. There is a trade-off increasing the HSR-Air passenger flow and the adjustments to the initial train timetable. In order to capture the complex feature interactions of passenger flow impact features, a novel HSR-Air passenger flow prediction model is proposed by using factorization machine and deep neural networks in this paper. Moreover, this passenger flow prediction model then is integrated into the train timetable rescheduling model to calculate the passenger flow under different HSR-Air service network. An approach based on a genetic algorithm is developed to solve the integrated model of deep learning and integer programming. The model and approach are tested in a real-world HSR-Air case in China with 15 HSR stations, 200 trains and 82 flights. The results show that the proposed model can obtain the satisfactory predictions and effectively enhance the HSR-Air passenger flow within an acceptable level of deviations to initial train timetable.