交通信息与安全2017，Vol.35Issue(5)：55-61,82,8.DOI:10.3963/j.issn.1674-4861.2017.05.007
基于卡尔曼滤波的城市快速路交通密度估计与拥堵识别
A Traffic Density Estimation and Congestion Identification of Urban Freeways Based on Kalman Filter
摘要
Abstract
For the situation where only a part of traffic detectors are available to obtain traffic information in urban freeway networks,a Kalman filter based ona macroscopic traffic flow model is studied in order to accurately estimate traffic density,and moreover,to quickly identify traffic congestion of all road sections.A macroscopic traffic flow model of urban freeway networks is developed by combining Dynamic Graph Hybrid Automata (DGHA) with Cell Transmission Model (CTM),and a Piecewise Affine Linear System (PWALS) model is deduced.Traffic density is estimated in the switched Kalman filter designed by this model,and congestion of urban freeway networks can be identified by comparing the road density estimation with the critical congestion density.The experiment takes Jingtong freeway in Beijing as a case study,and the Mean Absolute Error (MAE) which is generated by estimated value and actual value is 0.625 988.The resuits indicate the effectiveness of the proposed method.关键词
交通安全/交通密度估计/卡尔曼滤波器/动态图混杂自动机/拥堵识别Key words
traffic safety/traffic density estimation/Kalman filter/Dynamic Graph Hybrid Automata/congestion identification分类
交通工程引用本文复制引用
张驰远,陈阳舟,郭宇奇..基于卡尔曼滤波的城市快速路交通密度估计与拥堵识别[J].交通信息与安全,2017,35(5):55-61,82,8.基金项目
国家自然科学基金项目(61573030,61511130044)资助 （61573030,61511130044）
