As technologies continue to mature, the concept of IntelliDrive has gained significant interest. Besides its application on 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.
Since the change of density on arterials can help traffic engineers to track the queue length at intersections, which is important for traffic signal optimization, in this project we will focus on the estimation of traffic density on urban arterials when trajectories from a small percentage of vehicles are available. Most previous work, however, focuses on freeway density estimation based merely on detector data. In this research, we adopt the MARCOM (Markov Compartment) model developed by Davis and Kang (1994) to describe arterial traffic states. We then implement a hybrid extended Kalman filter to integrate the approximated MARCOM with fixed-point and vehicle-trajectory measurements. We test the proposed model 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.