Application of Aerial Video for Traffic Flow Monitoring and Management

Researchers at the University of Arizona are investigating the potential of remotely sensed data, using aerial video, to enhance existing data sources and therefore to improve traffic management. This research has developed new methods for both data collection and image processing of remotely sensed data. While the use of remotely sensed data to monitor traffic flows is not new, this research is examining the integration of digital video, global positioning systems (GPS), and automated image processing to improve the accuracy and costeffectiveness of data collection and reduction. Several different aerial platforms are under investigation for the data collection. With these platforms, a number of experiments in which aerial video and GPS data were collected from a major arterial and from a freeway are described. A technique has been developed to process the GPS data and the digital imagery, in near real time, to estimate vehicle speeds directly from the video images. The algorithm is capable of detecting vehicles and measuring their speeds, using image registration and edge detection techniques. When vehicles are matched between two frames, their speeds can be estimated directly. In addition, similar techniques are being used to derive other traffic flow parameters, such as densities, travel times, delays, turning counts, queue lengths, and measures of platoon dispersion. The automated image processing significantly reduces the amount of time needed to generate these traffic data.