Estimation Markov Decision Process of Multimodal Trip Chain between Integrated Transportation Hubs in Urban Agglomeration Based on Generalized Cost

Abstract

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.