Simultaneous Traffic Count and Ramp Flow Estimation for Multilane Freeways Based on Markov Models

被引：0

作者：

Ma, Yong ^{[1
]}

Zhang, Liguo ^{[1
]}

Zhang, Lu ^{[2
]}

机构：

[1] Beijing Univ Technol, Sch Elect & Control Engn, Beijing 100124, Peoples R China

[2] Beijing Traff Management Bureau, Beijing 100037, Peoples R China

来源：

2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2013年

关键词：

Traffic counts; Off-ramp flow; Kalman filtering; Simultaneous input and state estimation; Markov model; STATE ESTIMATION; UNKNOWN INPUTS; LINEAR-SYSTEMS; KALMAN FILTER; OBSERVERS; DESIGN; SPEED; LINKS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：276 / 281

页数：6

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