, Former Professor, Civil, Environmental and Geo-Engineering
As technologies continue to mature, the concept of IntelliDrive has gained significant interest. Besides its application to traffic safety, IntelliDrive also has great potential to improve traffic operations. In this context, an interesting question arises: If the trajectories of a small percentage of vehicles (IntelliDrive vehicles) can be measured in real time, how can we use such data to improve traffic management? This research serves as a starting point that aims to produce a paradigm shift to optimize the traffic signal control from the use of the conventional fixed-point loop detector data to the use of mobile vehicle trajectory-based data.
Changes in density on arterials can help traffic engineers track the queue length at intersections, which is important for traffic signal optimization. Although most previous work focuses on freeway density estimation based merely on detector data, this project focuses on the estimation of density when trajectories from a small percentage of vehicles are available.
In this research, the MARCOM (Markov Compartment) model developed by Davis and Kang (1994) is used to describe arterial traffic states. A hybrid extended Kalman filter is then used to integrate the approximated MARCOM with fixed-point and vehicle-trajectory measurements. The model is tested on a single signal link simulated using VisSim. Test results show that the hybrid extended Kalman filter with vehicle-trajectory data can significantly improve density estimation.
- Project number: 2008074
- Start date: 04/2008
- Project status: Completed
- Research area: Transportation Safety and Traffic Flow
Data and modeling