考虑乘客出发时刻的城市轨道列车时刻表优化

摘要

With full consideration of passenger choice behavior of departure time,a bi-level optimization model was proposed,with train departure schedule at the start terminal of an urban rail line as decision variable.In the upper level of the model,a train timetable optimization model was established,with the minimization of the comprehensive cost of both passengers and operators as the goal and headway,first and last departure time of trains,and service level as constraints.In the lower level,based on the full consideration of the interplay of choice behavior between passengers,an equilibrium transit assignment model for passenger departure time choice behavior was proposed with the constraint of train capacity,in order to effectively reflect the effect of timetable on passenger departure choice behavior.To solve the problem,genetic algorithm and MSA were in-troduced based on the characteristics of model.The validity of the proposed model and solution algorithm was verified through a numerical example,while a sensitivity analysis was conducted on the desired arrival time of passengers.Results showed that the proposed model can reduce passenger travel cost and total system cost more effectively than existing optimal methods.The more the desired arrival time of passengers disperses,the less the phenomena of train overload and underload occur,resulting in reduced system cost,passenger travel cost,and operational cost.%针对单条城市轨道线路,充分考虑乘客出发时刻选择行为,以列车始发站发车时刻为决策变量,构建城市轨道列车时刻表双层优化模型.上层以运营单位及乘客综合费用最小为目标,以列车发车间隔、首末班车时刻、运输供给水平等为约束,建立列车时刻表优化模型;下层以列车容量为强约束条件,在充分考虑个体乘客选择行为相互作用的基础上,建立考虑乘客出发时刻选择的均衡配流模型,从而有效反映列车开行方案对乘客出发时刻选择的影响.根据模型特点,设计遗传算法和MSA算法对上、下层模型求解.通过算例验证模型及算法的有效性,并对乘客期望到达时刻进行灵敏度分析,结果表明:与既有优化方法相比,本文模型能够更为有效地降低乘客出行费用及系统总费用;随乘客期望到达时刻离散化程度的提高,列车超载与低载客现象减少,综合费用、乘客出行总费用及运营费用将下降.