Principal Investigator(s):
Michael Levin, Associate Professor, Civil, Environmental and Geo-Engineering
Co-Investigators:
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John Hourdos, Former Research Associate Professor, Civil, Environmental and Geo-Engineering
Project summary:
Basic safety messages (BSM) contain data about a vehicle's position, speed, and acceleration. Roadside receivers (RSUs) can capture BSM broadcasts and translate them into information about traffic conditions. If every vehicle is equipped with awareness, BSMs can be combined to calculate traffic flows, speeds, and densities. These three key parameters will be post-processed to obtain queue lengths and travel time estimates. The project team proposed a traffic state estimation algorithm using BSMs based on the Kalman filter technique. The algorithm's performance was tested with BSMs generated from several arteria in a microscopic simulation model and BSMs generated with radar data collected on freeway sections. Then the project team developed a traffic monitoring system to apply the algorithm to a large-scale network with different types of roads. In the system, computers could remotely access the online server to acquire BSMs and estimate traffic states in real time.