基于站外限流与时刻表调整的地铁换乘站大客流协同控制

针对高峰期地铁换乘站站台容易发生拥挤问题,在分析乘客进站及换乘走行过程的基础上,构建了站外限流与列车时刻表协同优化模型。该模型可在满足站台安全容量约束下尽可能降低站外限流率,实现站外站内乘客加权人均等待时间最小。采用混沌遗传算法求解模型,并以惠新西街南口站为例,对本文模型、优化前方案和既有只限流方法进行对比分析。结果表明:与优化前方案相比,模型所得方案可满足站台安全容量约束,加权人均等待时分减少了21.2%;与只限流方法相比,模型可使站外平均限流率从36%下降到7.3%,加权人均等待时分减少了43.8%,且模型方案的各项指标在乘客走行时分发生波动的情形下表现更稳定。

Integrated Model on Inbound Passenger Flow Control and Timetable Regulation at Transfer Station

Based on the analysis of walking process of inbound and transfer passengers,an integrated model consisting of inbound passenger flow control and timetable regulation was proposed to relieve platform jam at transfer stations.The model aims to minimize the average waiting time of both inbound and transfer passengers taking into account the capacity limit of station platform.The chaos genetic algorithm was applied to solve the proposed model.In the case of Huixinxijienankou Station,a comparative analysis was made on the performance among the integrated model,the scheme of limiting inbound passengers only and the method of no measures on passenger flow control.The results show that the average waiting time of passengers with the integrated model is reduced by 21.2%compared with no passenger control strategy.When compared to the scheme of limiting the inbound passengers only,the proportion of inbound passengers kept outside the station drops from 36%to 7.3%,and the average waiting time of passengers is reduced by 43.8%.Extensive cases verify the stable performance of the solution by the proposed model when the deviation on passenger walking time is considered.