Abstract:

To address the current gaps in managing the restricted passenger flow transfers in urban rail transit networks, this study investigates the planning of transfer vehicle routes and capacity allocation under specific flow control conditions. First, the utility and selection probability of passengers opting for transfer vehicles are analyzed and quantified across various route conditions. Then, a model for the design of transfer bus routes and capacity planning under flow control scenarios is proposed, aiming to minimize total expected travel time, minimize 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 is divided into two subproblems: route optimization and service optimization. The first subproblem is transformed into a traveling salesman problem, with the resulting alternative route paths serving as input for solving the second subproblem. Using passenger flow data from the Chengdu rail transit network during peak morning hours, the method is validated under varying flow control intensities. The study also explores preferences in transfer routes based on the number of stops and the selection of stop locations. Results indicate that the routes yielding optimal objective values generally consist of paths with two to three stops, showing high concentration in stop selection and a strong preference for three to four specific routes. As flow control intensity increases, there is a marked preference for routes with fewer stops and shorter travel distances to meet rapid transfer demands. In terms of service frequency, a linear growth trend is observed overall, however, when flow control intensity exceeds 0.8, a single route can no longer meet transfer demands, leading to a departure from the linear growth trend.

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