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.
Liu, Henry X.; Di, Xuan.
Development of Algorithms for Travel Time-Based Traffic Signal Timing, Phase I – A Hybrid Extended Kalman Filtering Approach for Traffic Density Estimation along Signalized Arterials.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.