Optimization of passenger flow control and parallel bus bridging in urban rail transit based on intelligent transport infrastructure

Abstract

Passenger flow control and bus bridging are used widely in the operations and management of urban rail transit to relieve the pressure of urban rail transit passenger flow, especially in peak periods. This paper presents an optimization method based on time-varying running time in links. We first develop a mixed integer nonlinear programming model seeking to achieve the minimum total passenger travel time and operation cost. An optimization network and an algorithm are then designed to solve the model. We use the developed method to solve both a small-scale simulated case study and a real-world case study involving the Chengdu Metro. The results obtained by the designed algorithm are comparable with those obtained by the CPLEX solver but with a shorter calculation time. The results show that parallel bus bridging can effectively reduce the number of waiting passengers. A sensitivity analysis of weight suggests that the algorithm successfully balances passenger travel cost and operating cost while incorporating time-varying running times leads to more realistic and dynamic infrastructure planning. This work contributes to the development of intelligent urban rail and road infrastructure systems, promoting safer and more efficient public transport operations.