Urban Rail Transit Passenger Flow Induction Optimization Under Event Interference
Urban rail transit systems often experience operational disruptions or reduced service capacity during peak hours,major events,and adverse weather conditions.To effectively mitigate the negative impacts of these disruptions on passenger flow and enhance the resilience of urban rail transit systems,this paper proposes an optimization method for passenger flow guidance in response to disruptive events.First,considering the impact of disruptive events and the compliance rate of passenger guidance,this paper develops a rail transit passenger flow guidance model with the goal of minimizing the total travel time of passengers in the system.Then,a column generation-based exact algorithm is designed,and Gurobi is used to solve the restricted master problem.The A*algorithm is applied to solve the pricing subproblem,and the branch-and-bound algorithm is utilized to find integer solutions.Through actual case analysis,it is found that the acceleration strategies designed in this paper can improve the solving efficiency by 66%～89%,with performance significantly superior to using Gurobi alone.Simulations of scenarios ranging from minor to severe disruptions demonstrate that the proposed optimization method is applicable to urban rail transit passenger flows of varying scales,effectively guiding passenger travel paths under various disruption intensities.
万方数据
维普
