Real-time road traffic state prediction based on ARIMA and Kalman filter

作     者：Dong-wei XU Yong-dong WANG Li-min JIA Yong QIN Hong-hui DONG 

作者机构：College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China United Key Laboratory of Embedded System of Zhejiang Province Hangzhou 310023 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044 China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2017年第18卷第2期

页      面：287-302页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 082303[工学-交通运输规划与管理] 081201[工学-计算机系统结构] 082302[工学-交通信息工程及控制] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：Project supported by the National Science &Technology Pillar Program(No.2014BAG01B02) 

主　　题：Autoregressive integrated moving average （ARIMA） model Kalman filter Road traffic state Real-time Prediction 

摘      要：The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average （ARIMA） and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.