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A crucial component of multimodal transportation networks and long-distance travel chains is the forecasting of transfer passenger flow between integrated hubs in urban agglomerations, particularly during periods of high passenger flow or unusual weather. Deep learning is better suited to managing massive amounts of traffic data and predicting extended time series. In order to solve the problem of gradient explosion or gradient disappearance that recurrent neural networks are prone to when dealing with long time sequences, this study used a transformer prediction model to estimate short-term transfer passenger flow between two integrated hubs in an urban agglomeration and a long short-term memory network to incorporate previous historical data. The experimental analysis uses two sets of transfer passenger data from the Beijing-Tianjin-Hebei urban agglomeration, collected every 30 min in May 2021 on the transfer corridors between an airport and a high-speed railway station. The findings demonstrate the high adaptability and good performance of the suggested model in passenger flow forecasting. The suggested model and forecasting outcomes assist management in making capacity adjustments in time to correspond with changes, enhance the effectiveness of multimodal transportation systems in urban agglomerations and significantly enhance the service of long-distance multimodal passenger travel.

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An efficient multimodal transportation network is crucial to the development of urban agglomeration. Rapid transfer of travelers between integrated transportation hubs is essential for long-distance multimodal trip chains. The Markov Decision Process framework was estimated to explore the optimal transfer trip chain of different income groups, given that the states are considered nodes between hubs, the reward functions are calculated by using the generalized travel cost between states after travelers make action decision, and the actions between states contain bus, subway, taxi, and walk. The optimal trip chain can be obtained through a value iteration algorithm. In the case study, multimodal transfer trip chains of different types between Beijing Capital International Airport and Beijingxi Station in Beijing-Tianjin-Hebei urban agglomeration were constructed by MDP to compare the optimal trip chains of various groups. The findings of this study are as follows: (1) long-distance travelers always prefer to choose the unimodal fewer transfers trip chain between hubs; (2) long-distance travelers are more likely to choose the trip chain with more transfers more than long waiting time; (3) individual income difference affects the generalized cost of trip chains and also influences the optimal choice of trip chain through the MDP framework. One potential application of this study is to complement the research on the transfer behavior of multimodal trip chains in long-distance travel, which can be used to help management alleviate the excessive pressure of passenger flow between integrated hubs due to the sudden colossal travel flow during severe weather days or holidays.

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