Mobile traffic surveillance system for dynamic roadway and vehicle traffic data integration

Abstract

Embedded vehicle detector sensor systems used in today's roadways provide a direct measurement of traffic flow, roadway occupancy, and average speed. This type of sensor network does not directly measure traffic density; instead it is estimated from the other measured parameters. In this paper, we have developed systematic techniques to measure traffic conditions by utilizing both on- and off-board computer vision systems. A unique development technique is a combined computer vision and Global Positioning System (GPS)-equipped mobile traffic surveillance system to measure localized traffic density. In addition, we correlate the localized density measurement from the mobile system with the flow estimates from an embedded vehicle detector sensor system using a space-time diagram. Experiments have shown the complementary nature of these sensing techniques. We believe that with the increasing use of on-board vision sensors, more and more localized traffic information samples can be reported to this type of database. The combined analysis of temporal-spatial variable density and the embedded loop sensor data will provide a better and more reliable method for traffic condition estimation and prediction.