Identification method of crowded passenger flow based on automatic fare collection data of Nanjing Metro

To relieve traffic congestion in urban rail transit stations,a new identification method of crowded passenger flow based on automatic fare collection data is proposed.First,passenger travel characteristics are analyzed by observing the temporal distribution of inflow passengers each hour and the spatial distribution concerning cross-section passenger flow.Secondly,the identification method of crowded passenger flow is proposed to calculate the threshold via the probability density function fitted by Matlab and classify the early-warning situation based on the threshold obtained.Finally,a case study of Xinjiekou station is conducted to prove the validity and practicability of the proposed method.Compared to the traditional methods,the proposed comprehensive method can remove defects such as efficiency and delay.Furthermore,the proposed method is suitable for other rail transit companies equipped with automatic fare collection systems.

城市轨道交通动态客流分配仿真方法研究

根据轨道交通网络存在大量换乘路径的特点,改进深度优先搜索算法得出站点间换乘路径的有效出行时间。基于自动票务收集系统(automatic fare collection system,AFC)数据得到的乘客进出闸机时刻,利用仿真方法确定乘客与列车在时间和路径的接续关系,同时考虑始发乘客和换乘乘客路径选择行为的差异,将二者区分配流。动态更新先到乘客利用换乘路径的出行时间,并以更新后的时间作为后续出发乘客的路径选择依据。结果表明,该仿真方法可以有效反映乘客的出行过程,具有较高的配流精度。