A Dempster-Shafer Sensor Fusion Approach for Traffic Incident Detection and Localization

摘要

Traffic incident detection and localization is an important application in traffic management systems. The ability to detect and localize traffic incidents enables a timely response to accidents and facilitates effective and efficient traffic flow management. This paper presents a sensor-network based approach for tackling the problem of incident localization. Traffic count sensors, which tend to be an element of the road infrastructure, are used as the source of traffic sensory data. Such sensors come in a variety of types and capabilities, providing the potential for complementary and redundant information gathering. Thus, it is conceivable to fuse such sensory information to achieve insightful and accurate incident detection and localization. In this context, the Dempster-Shafer (DS) theory of evidence is used as the foundation for fusing traffic sensory data. In this paper, a traffic model generator and two traffic-counting sensory systems are employed for acquiring traffic data pertinent to the distribution of cars on a given road segment. Experimental analysis on the performance of the proposed approach is provided.