摘要
城市轨道交通线路作为城市最大的单体建筑,连接着城市各处重要的客流集散地,在大型活动或其他重要的非常规场景,由于出行客流的聚变,很容易发生不同程度的大规模客流突发性聚集或消散,从而对乘客出行和线网运营产生直接或间接的影响。传统的轨道交通客流分析方法主要是通过站点工作人员的经验和感觉给出,无法从海量的乘客历史出行大数据中提取与挖掘乘客出行规律和特征。基于大数据的客流分析方法,结合实时进出站客流信息,推演乘客出行信息,获取路网精细化客流分布和动态信息,提供准确的客流拥挤度信息。通过对高风险点客流来源和乘客出行过程反向推演,客流来源站的进站管控,提供路网大客流精准管控方法。通过乘客历史出行记录分析乘客个体出行规律和特征,为乘客提供个性化信息服务,实现精准信息推送。有效运用客流数据,全面满足智慧运营管理模式的要求,快速实现智慧化的客流管理。
Urban rail transit line is the largest single building in the city,which connects important passenger flow distribution centers all over the city.In large-scale events or other important unconventional scenes,due to the fusion of passenger flow,it is easy for large-scale passenger flow to suddenly gather or dissipate in different degrees,thus directly or indirectly affecting passenger travel and network operation.The traditional passenger flow analysis method of rail transit is mainly given through the experience and feeling of station staff,it is unable to extract and mine the passenger travel rules and characteristics from the massive historical travel data of passengers.Based on the passenger flow analysis method of big data,combined with the real-time passenger flow information,deduced the passenger travel information,obtained the refined passenger flow distribution and dynamic information of the road network,and provided accurate passenger congestion information.Through the reverse deduction of passenger flow source and passenger travel process at high-risk points,the inbound control of passenger flow source stations provides accurate control methods for large passenger flow in the road network.The individual travel rules and characteristics of passengers through historical travel records are analyzed,personalized information services for passengers are provided,and accurate information push is realized.The method effectively use passenger flow data,fully meet the requirements of intelligent operation and management mode,and quickly realize intelligent passenger flow management.
作者
杨广禄
Yang Guanglu(Shenzhen Das intelligent Co.,Ltd.,Shenzhen,Guangdong 518000,China)
出处
《机电工程技术》
2022年第10期230-234,共5页
Mechanical & Electrical Engineering Technology
关键词
大数据
客流监测
多粒度分析
指标计算
big data
passenger flow monitoring
multi granularity analysis
index calculation
