Trajectory Based Traffic Control with Low Penetration of Connected and Automated Vehicles

Abstract

The state-of-the-practice real-time signal control strategies rely heavily on infrastructure-based sensors. With the advances in connected vehicle (CV) technologies, real-time vehicle trajectory data are reported to the traffic control system. The new source of data provides a much more complete picture of the traffic conditions around the intersection so that traffic controllers should be able to make “smarter” decisions. However, most of the existing connected vehicle (CV)-based traffic control models require a critical CV penetration rate of around 25%. This project aims to develop new models of vehicle trajectory-based real-time traffic control under low penetration of CVs (<10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function (i.e., total delay). The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates.