【作者】 梁小虎； 陈曦；

【Author】 Xiaohu Liang;Xi Chen;Center for Intelligent and Networked System,Department of Automation,Tsinghua University;

【机构】 清华大学自动化系智能与网络化系统研究中心；

【摘要】 随着城市轨道交通网络的扩大,优化列车时刻表以提升乘客体验和运营效率愈发重要。本文面向大规模轨道交通网络,基于OD (Origin-Destination)客流数据建立了一个以列车发车间隔为决策变量、以固定时段内全体乘客总等待时间为目标的整数非线性优化模型。为提高求解效率,本文提出一种基于遗传算法框架、结合站点删减机制的高效优化方法。该方法通过剔除部分非换乘、低客流中途站,在保留主要客流特征的同时降低计算复杂度。以某城市地铁网络为算例,探究了所提方法在不同删减方案和客流场景下的优化效果和计算效率。实验结果表明,在保证较优服务质量的前提下,站点删减可有效加速时刻表优化过程。更多还原

【Abstract】 With the expansion of urban rail transit networks,optimizing timetable to enhance passenger experience and operational efficiency has become increasingly important.This study focuses on large-scale rail transit networks and proposes an integer nonlinear optimization model based on OD(Origin-Destination) matrix passenger flow data.The decision variables are train headway and the objective function is total passenger waiting time.To improve computational efficiency,a highperformance optimization method is designed under the framework of genetic algorithm,incorporating a station reduction mechanism.By eliminating certain non-transfer and low-demand intermediate stations,the method reduces computational complexity while preserving major passenger flow characteristics.One case study based on the real data is conducted to evaluate the optimization performance and computational efficiency under different reduction schemes and passenger demand scenarios.Experimental results show that this method significantly accelerates the optimization process while maintaining a high level of service quality.更多还原