Urban Rail Transit Passenger Flow Control Considering Efficiency and Fairness
Passenger overcrowding during peak hours is a critical challenge in urban rail transit of large cities.It not only increases the risk of passenger stampede accidents within stations but also leads to significant differences in waiting times between upstream and downstream stations.Considering the stochastic and dynamic nature of passenger demand,this paper develops a multi-objective passenger flow control optimization model that aims to enhance travel efficiency and improve passenger travel fairness.The model can determine reasonable boarding ratio of passengers at each station,and optimize the average waiting time of all passengers(efficiency)and the Euclidean distance between the average waiting time of passengers at each station(fairness)and the ideal value under the feasibility constraints.To solve this multi-objective stochastic optimization model,an online optimization algorithm is developed to make passenger flow control decisions for each demand scenario.Numerical experiments based on passenger flow data from Beijing Metro Line 5 show that,compared to the benchmark first-come-first-served policy,the proposed method can significantly improve operational efficiency and fairness.Moreover,the algorithm achieves results close to those obtained by direct solving with Gurobi while reducing the computation time by 76.3%.This paper provides an effective strategy and methodological reference for addressing the problem of passenger overcrowding in urban rail transit during peak hours.
万方数据
维普
