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基于AFC数据的南昌轨道交通车站精细化分类
引用本文:王 晨,石俊刚,席苏路,包佳瑶.基于AFC数据的南昌轨道交通车站精细化分类[J].都市快轨交通,2023,36(6):49-56.
作者姓名:王 晨  石俊刚  席苏路  包佳瑶
作者单位:华东交通大学交通运输工程学院,南昌 330013;1. 华东交通大学交通运输工程学院,南昌 330013;2. 同济大学交通运输工程学院,上海 201804;南昌轨道交通集团有限公司运营分公司,南昌 330038;1. 华东交通大学交通运输工程学院,南昌 330013;2. 南昌轨道交通集团有限公司运营分公司,南昌 330038
基金项目:南昌轨道交通科研计划项目(2021HGKYC005);国家自然科学基金(72361012)
摘    要:为满足城市轨道交通车站精细化客运组织需求,对车站进出站客流特性进行有效的分类管理。结合自动售检票系统(auto fare collection,AFC)采集的进出站客流数据,从车站进出站客流总量及时序特性方面入手,提出一种基于K-means算法的双层规划聚类方法对全线所有车站进行聚类并划分车站类型。首先以车站进出站客运总量为特征指标进行上层聚类,得出不同客运规模的车站大类;然后考虑车站进出站客流的时变特征,根据不同时段内的客流变化特点构建特征向量进行下层聚类,识别车站客流的时序分布特性。研究结果表明:利用本算法得到的分类结果与实际高度吻合,不同类别车站在客运规模和时变特性上差异明显。双层K-means聚类分析算法通过把握客运规模和客流时变特征,对车站进行精细划分,为车站的客运组织提供依据。

关 键 词:地铁车站  客流特征  双层K-means  精细化分类  自动售检票

Accurate Classification of Nanchang Urban Rail Transit Stations Using AFC Data
Institution:College of Transportation Engineering, East China Jiaotong University, Nanchang 330013;1. College of Transportation Engineering, East China Jiaotong University, Nanchang 330013; 2. College of Transportation Engineering, Tongji University, Shanghai 201804;Nanchang Rail Transit Group Co. Ltd., Nanchang 330038; 1. College of Transportation Engineering, East China Jiaotong University, Nanchang 330013;2. Nanchang Rail Transit Group Co. Ltd., Nanchang 330038
Abstract:To meet the demands of meticulous passenger transportation organizations in urban rail transit stations, it is necessary to classify and manage stations based on the characteristics of entering and exiting passenger flows. Based on the entering and exiting passenger flow data collected by the automatic fare collection (AFC), this study proposes a two-layer planning clustering method based on the K-means algorithm to cluster and classify all stations on the entire line from the aspects of the total amount of passenger flow and the temporal characteristics of entering and exiting passenger flows. First, upper-layer clustering was carried out based on the total number of passenger flows entering and exiting. Using this characteristic indicator, different types of stations with various passenger transport scales were identified. Then, considering the temporal characteristics of entering and exiting passenger flows, a feature vector was constructed according to the characteristics of time-varying passenger flow in different periods for lower-layer clustering to identify the temporal distribution characteristics of passenger flow in the station. The classification results were highly consistent with the actual situation, and there were significant differences in the passenger transport scale and temporal characteristics among the stations of different categories. It can be seen that the proposed two-layer K-means clustering analysis algorithm can well grasp the passenger transport scale and temporal characteristics of passenger flow providing a basis for the passenger transportation organization of stations.
Keywords:station passengers  flow characteristics  double-layer k-means  accurate classification  auto fare collection
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