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Analysis of Dynamic Passenger Flow in Urban Rail Transit Based on Data Mining

机译：基于数据挖掘的城市轨道交通动态客流分析

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摘要

According to the more qualitative methods for urban rail analysis, long cycle of diagramming train schedule chart and large passenger flow, getting the real-time statistics of the passengers' IC card information in AFC system, according to the retention time in each station, a scatterplot of the passengers' density can be drawn, intuitively reflecting the distribution of the passengers' density in every station. To gain the data of the passenger flow in each station and on each route, a more accurate full-time traffic planning can be made by using the reliable traffic data. Due to the multi-routes for passengers, Based on the optimization of Dijkstra, the route in a net can be chosen, then mapping the train schedule chart. At the same time, use the data mining technology in large data era to process the history data and the dynamic information of passenger flow to provide accurate and intuitive reference for the update of the train schedule chart.
机译：根据更优质的城市轨道交通分析方法，较长的列车时刻表图表绘制和大量的客流，根据每个车站的停留时间，获取AFC系统中乘客IC卡信息的实时统计信息，可以绘制乘客密度的散点图，直观地反映每个站点中乘客密度的分布。为了获得每个站点和每个路线上的客流数据，可以使用可靠的交通数据来制定更准确的全时交通计划。由于乘客有多条路线，因此，根据Dijkstra的优化，可以选择网络中的路线，然后映射火车时刻表。同时，利用大数据时代的数据挖掘技术对历史数据和客流动态信息进行处理，为列车时刻表的更新提供准确，直观的参考。

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