铁路通信信号工程技术2018，Vol.14Issue(1)：50-55,6.DOI:10.3969/j.issn.1673-4440.2018.01.012
基于视频检测的轨道交通短时客流预测研究
Short-time Passenger Flow Prediction of Rail Transit based on Video Frequency Detection
尹嵘 1张炳森 2张宁 2徐文3
作者信息
- 1. 北京全路通信信号研究设计院集团有限公司,北京 100070||北京市高速铁路运行控制系统工程技术研究中心,北京 100070
- 2. 东南大学智能运输系统研究中心轨道交通研究所,南京 210096
- 3. 北京城建设计发展集团股份有限公司,北京 100037
- 折叠
摘要
Abstract
In order to improve the accuracy of passenger flow state prediction,a multivariate time series forecasting model is proposed for the forecast of the passenger flow state.For the research on short-time passenger flow forecast based on video frequency,it adopts the directional gradient histogram feature descriptor and support vector machine detection to identify pedestrian targets,and uses the Camshift algorithm to track the target,so as to access the parameters of passenger flow and speed,and then establish the vector error correction model according to the co-integration relation between parameters.Finally,validation and comparative analysis are carried out using 4A channel video frequency data measured at Gulou station in Nanjing,the results show that the model constructed in this paper has a better prediction performance.The MAPE value of both the passenger flow and speed is less than 8%,which is better than the ARIMA(0,1,1)prediction performance established by the same sample data.关键词
城市轨道交通/短时预测/向量误差修正模型/性能评估Key words
urban rail transit/short-time prediction/vector error correction model/performance evaluation分类
交通工程引用本文复制引用
尹嵘,张炳森,张宁,徐文..基于视频检测的轨道交通短时客流预测研究[J].铁路通信信号工程技术,2018,14(1):50-55,6.
