基于南京地铁AFC数据的大客流识别方法（英文）

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

为了缓解城市轨道交通车站的交通拥堵问题,提出了一种基于自动售检票设备刷卡数据的大客流识别方法.首先,通过观察每小时进站乘客的时间分布和断面客流量的空间分布,分析乘客出行特征.其次,提出了大客流的识别方法.根据高峰时段的五分钟客流量进行频率直方图分析,通过Matlab拟合的概率密度函数计算阈值,根据获得的阈值划分预警等级.最后,以新街口站为例,证明了该识别方法的有效性和实用性.结论表明,与传统方法相比,所提出的综合方法消除了效率和延迟等缺陷.此外,所提方法也可适用于其他配备了自动售检票系统的地铁公司.

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.