A Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

作     者：孔庆杰 刘允才 KONG Qing-jie , LIU Yun-cai(Inst. of Image Processing and Pattern Recognition, Shanghai Jiaotong Univ., Shanghai 200240, China)

作者机构：Inst. of Image Processing and Pattern Recognition Shanghai Jiaotong Univ. 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2007年第12卷第6期

页      面：793-798,804页

学科分类：08[工学] 0838[工学-公安技术] 

基　　金：STCSM Foundation of China ( No052112021) 

主　　题：traffic state estimation D-S evidence theory information fusion intelligent transportation systems 

摘      要：In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.