Abstract:

To address the current lack of attention to the transfer of restricted passenger flows under urban rail transit network flow control scenarios, this study investigated the routing and capacity allocation of transfer vehicles under specific flow control conditions. Firstly, the utility and selection probability of passengers opting for transfer vehicles were analyzed and quantified across various route conditions. Then, a model for the design of transfer bus routes and capacity planning under flow control scenarios was proposed, aiming to minimize total expected travel time and the operational costs of transfer vehicles, and maximize the alleviation of passenger congestion in the rail transit network. To enhance model-solving efficiency, the model was divided into two subproblems: route optimization and service optimization. The first subproblem was transformed into a traveling salesman problem, with the resulting alternative route paths serving as input for solving the second subproblem. Based on Chengdu’s urban rail transit network and passenger flow data during the morning peak period, the effectiveness of the proposed model under different levels of flow restrictions was verified, and the preferences for the number of stops and the selection of transfer station locations were discussed. Results indicate that routes with 2 to 3 stops generally perform well in terms of the objective function, and the selection of stopping stations is highly concentrated, with a strong preference for 3 to 4 specific routes. As flow control intensity increases, there is a clear tendency to choose routes with fewer stops and shorter travel distances to meet rapid transfer demands. The number of scheduled trips increases approximately linearly overall; however, when the flow restriction intensity exceeds 0.8, a single route can no longer meet the transfer demand, and the linear growth trend no longer holds.

Key words: integrated transportation, passenger transit, collaborative optimization, metro passenger flow, passenger flow control, traveling salesman problem