基于自动售检票数据的轨道车站客流识别模型

为探究城市轨道交通车站客流模式,采用轨道自动售检票(automatic fare collection,AFC)数据,构建客流指标,提出了一种基于K-means聚类算法的站点客流识别模型.以重庆轨道3号线连续1个月的AFC数据为例,探讨工作日、周末、节假日时期不同客流指标和综合多变量指标的聚类结果.结果表明:不同时期客流指标能够促进车站客流识别;将站点客流模式分为7类时,聚类效能最佳;通过连续1周和连续1个月聚类结果对比,验证了分类结果具有良好的稳定性.结合结果数据特征和站点实际情况对车站客流特点进行归纳总结.

Passenger Flow Identification Model of Rail Station Based on AFC Data

In order to explore the passenger flow pattern of urban rail transit stations, AFC data was used to construct passenger flow indicators, and a station passenger flow recognition model based on K-means clustering algorithm was proposed. Taking the AFC data of Chongqing Rail Line 3 for 1 month as an example, the clustering results of different passenger flow indexes and comprehensive multi-variable indexes during working days, weekends and holidays are discussed. The results show that: passenger flow indexes in different periods can promote station passenger flow identification; When the station passenger flow pattern is divided into 7 categories, the clustering efficiency is the best; by comparing the clustering results for 1 week and 1 month, the classification results are verified to have good stability. The characteristics of the station passenger flow are summarized and combined with the characteristics of the resulting data and the actual situation of the station.