交通信息与安全2024，Vol.42Issue(1)：131-141,11.DOI:10.3963/j.jssn.1674-4861.2024.01.015
基于遗传算法的高峰时段车站协同限流方法
A Method for Coordinated Passenger Flow Control at Stations During Peak Period Based on Genetic Algorithms
摘要
Abstract
Passenger flow control at urban rail transit stations is an effective strategy for alleviating congestion dur-ing peak periods.However,existing measures often overlook cooperative relations among adjacent stations along the same line,indicating the need for further improvements to enhance its efficacy.In this paper,the interaction among passengers,trains,and stations is considered comprehensively.Train schedules are discretized during peak hours based on departure intervals at stations.These discrete time periods are utilized as the basis for our research and corresponding passenger arrival data are extracted accordingly.Taking into account both supply and demand considerations,optimization objectives focus on two primary aims of minimizing aggregate passenger delay time and maximizing passenger turnover volume.Considering the train transportation capacity,passenger flow control in-tensity,and station service level,the remaining train transportation capacity is introduced as a constraint to balance the passenger flow demand of different stations,and an optimization model station for coordinated station flow con-trol is constructed.Given the complexity in solving multi-objective functions,an embedded genetic algorithm is pro-posed to address conflicts among optimal solutions.Using Line 3 of the Nanjing Metro as a case study,a compara-tive analysis is conducted with the scenario without coordinated flow control(first-come-first-served)during peak hours.The results show that a 1%increase in total passenger turnover results in a 2.3%decrease in the number of pas-senger delays,a 4.3%decrease in total passenger delay time,and significant alleviations of delays at congested sta-tions,leading to a more balanced spatial and temporal distribution of delays.To verify the algorithm's effectiveness and the model's stability,the genetic algorithm is compared with the Gurobi solver,and the sensitivity of a key param-eter,the train load factor,is analyzed.The proposed genetic algorithm demonstrates better performance in addressing the dual optimization objective,thus aiding in the mitigation of significant passenger delays during peak hours.关键词
城市轨道交通/高峰客流/协同限流/遗传算法/运输能力Key words
urban rail transit/peak passenger flow/coordinated passenger flow control/genetic algorithm/transpor-tation capacity分类
交通工程引用本文复制引用
申梦君,董宁宁,李铁柱,郭竞文,刘慧..基于遗传算法的高峰时段车站协同限流方法[J].交通信息与安全,2024,42(1):131-141,11.基金项目
国家重点研发计划项目(2021YFE0112700)、江苏轨道交通产业发展协同创新基地开放基金项目(N0.GCXC2104)资助 （2021YFE0112700）
