Simultaneous Traffic Count and Ramp Flow Estimation for Multilane Freeways Based on Markov Models
作者单位：School of Electronic and Control Engineering Beijing University of Technology Beijing Traffic Management Bureau
会议名称：《第25届中国控制与决策会议》
主办单位：IEEE;NE Univ;IEEE Ind Elect Chapter;IEEE Harbin Sect Control Syst Soc Chapter;Guizhou Univ;IEEE Control Syst Soc;Syst Engn Soc China;Chinese Assoc Artificial Intelligence;Chinese Assoc Automat;Tech Comm Control Theory;Chinese Assoc Aeronaut;Automat Control Soc;Chinese Assoc Syst Simulat;Simulat Methods & Modeling Soc;Intelligent Control & Management Soc
会议日期：2013年
学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程]
基 金：supported by National Nature Science Foundation under Grant 61111130119 and 60904069 the Doctoral Fund of Ministry of Education of China under grant 20091103120008
关 键 词：Traffic counts Off-ramp flow Kalman filtering Simultaneous input and state estimation Markov model
摘 要：As a distributed parameter system, traffic flow model of freeway traffic is determined by the traffic state on-road and boundary flows form on-ramp or off-ramp sections. The existing studies for traffic estimation mainly focus on the traffic parameter, namely density （or vehicles） of mainline traffic. In this paper, Kalman filtering for simultaneous traffic counts and off-ramp flow estimation is proposed with the linearization of the speed density observation equation. The state-space model is formulated by using a Markov chain to describe the vehicles’ lane-change movements. Numerical studies are carried out to investigate the performance of the developed approach.