A new approach on passenger flow assignment with multi-connected agents

Efficient passenger flow assignment is essential for ensuring the safe and stable operation of the urban rail transit (URT). This paper proposes a new passenger flow assignment approach to address this challenge. The approach generates two agents: connected vehicle and unconnected passenger, to simulate vehicle operation and passenger travel for the URT network. It derives the passenger trajectory by the passenger route evolution mechanism to achieve the passenger flow assignment for the whole network. Furthermore, it provides statistics on platform passenger flow when obtaining the overall station passenger flow. Meanwhile, the approach employed Automatic Fare Collection (AFC) data as input to analyze the passenger flow features of the Shanghai Metro. The passenger flow assignment results can reveal the start and end of morning and evening peak hours at a time granularity of seconds. That is, 7:16:20 and 9:33:00 for the morning peak and 16:49:00 and 19:22:50 for the evening peak, respectively. The results also reveal the route choice pattern of passengers. Passengers have preferences for each type of route, in descending order: the shortest route, the minimum number of stations route, and the minimum number of transfer stations route, when they have a choice of these three routes. Moreover, the results can accurately identify large passenger flow stations and provide data support for station passenger flow management strategies.