, Assistant Professor, Civil, Environmental and Geo-Engineering
John Hourdos, Research Associate Professor, Civil, Environmental and Geo-Engineering
The National Highway Traffic Safety Administration (NHTSA) has proposed issuing a regulation requiring all new cars to have an Awareness device broadcasting basic safety messages (BSM) containing data about the car'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 an 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. Unfortunately, even if NHTSA does produce the proposed regulation today, it will take up to 10 years for all vehicles to either be manufactured or retrofitted with an Awareness device. Fortunately, by combining a partial market penetration with the right models and methodologies filling the blanks, it is possible to generate real-time traffic performance measures and develop the infrastructure for harnessing this technology state-wide.
This project filters speed and location information from BSMs through a mesoscopic traffic flow model to create a Traffic State Estimator (TSE), providing traffic measurements at high resolution. Given that only a tiny fraction of state-owned vehicles is currently capable of broadcasting BSMs, the problem is approached from two different directions. First, methodologies are being created to produce BSM-based traffic performance metrics by utilizing the Minnesota Traffic Observatory's (MTO) I-94 Connected Vehicles Testbed, developed through a Roadway Safety Institute (RSI) project. This provides emulated BSMs and actual vehicle trajectories. The testbed includes a half-mile section of I-94 WB seamlessly covered by seven 24GHz radar sensors, providing vehicle trajectories at 20Hz--twice the frequency of BSMs--for all vehicles on the mainline. The second direction involves the use of the Twin Cities metro-wide traffic simulation model owned by the MTO to develop and test the scaling-up of the aforementioned methodologies on a network level. By combining the developed TSE with simulation, the functional requirements for a system collecting BSMs from hundreds of thousands of vehicles over a large geographic area will be identified and a prototype of the BSM-based traffic performance measures methodologies will be tested.