Optimization of carriage parking based on simulation of passenger dynamics in the dynamic autonomous non-stop rail transit system

This study investigates the carriage parking problem in the dynamic autonomous non-stop rail transit (DANRT) system, with a particular focus on the movement behaviors of passengers. A cell transmission model (CTM) is formulated to depict the movement behaviors of passengers in the DANRT system and the interactions between passengers. The parameters in the CTM are calibrated by using a set of video recordings and reproducing the arching phenomenon of passengers. To optimize carriage parking schemes, a swarm intelligence-based heuristic algorithm is proposed, where the CTM is embedded into the evaluation process to dynamically assess passenger moving efficiency during each iteration. We conduct a set of numerical experiments to evaluate the effect of algorithm parameters on algorithm performance and the influence of passenger behaviors on passenger waiting times. The results demonstrate that the algorithm can further reduce the theoretical minimum total passenger waiting time obtained without considering passenger movement behaviors and interactions by about 6%. Additionally, overall system efficiency reaches its maximum when the frequency of carriage re-selection behavior of passengers remains at a moderate level. Moreover, it is essential to deliberately designate carriages for passengers to improve the travel efficiency of passengers in the DANRT system.