考虑铁路换乘客流的地铁列车发车时刻与限流方案协同优化研究

Study on Collaborative Optimization of Subway Train Departure Time and Current Limiting Scheme Considering Railway Transfer Passenger Flow

由于铁路换乘地铁的客流较多且到达密集,衔接铁路的地铁车站一般采取限流措施以保证站内人数不超过安全允许值.同时,如果地铁列车发车时刻与干线铁路列车到达时刻不能很好地匹配,会导致换乘客流在衔接地铁站内总停留时间长、站台聚集人数过高.为此,文中基于铁路列车到达情况,构建换乘地铁的客流规律计算模型.在此基础上,以换乘客流在地铁站内的总停留时间最小为目标,建立地铁列车发车时刻与限流方案协同优化模型,并设计遗传算法进行求解.案例结果表明:与等间隔发车且无限流方案相比,协同优化方案可减少换乘客流的站内总停留时间5%以上,且站台最高聚集人数处于安全水平;与单独优化限流方案或发车时刻相比,协同优化方案可至少多节约站内总停留时间9.7%和1.5%.

Due to the large number of passengers transferring from railway to subway and the dense arrival,the subway stations connecting the railway generally adopt current limiting measures to ensure that the number of people in the stations does not exceed the safe allowable value.Meanwhile,if the departure time of the subway train and the arrival time of the trunk railway train cannot match well,the total stay time of the transfer passenger flow in the connecting subway station will be long and the number of people gathered on the platform will be too high.Therefore,based on the arrival situation of railway trains,this paper constructed a calculation model of passenger flow law for transferring to subway.On this basis,aiming at minimizing the total stay time of the transfer passenger flow in the subway station,a collaborative optimization model between the departure time of the subway train and the current limiting scheme was established,and a genetic algorithm was designed to solve it.The case results show that the collaborative optimization scheme can reduce the total stay time of the transfer passenger flow by more than 5%compared with the scheme of starting at equal intervals and infinite flow,and the maximum number of people gathered at the platform is at a safe level.Compared with the current limiting scheme or departure time alone,the collaborative optimization scheme can save at least 9.7%and 1.5%of the total stay time in the station.