Traffic Signal Optimization with Connected Vehicle Trajectories

Abstract

Traffic signal re-timing is one of the most cost-effective methods for reducing congestion and energy consumption in urban areas based on the existing road infrastructure. However, high installation and maintenance costs of vehicle detectors have prevented the widespread implementation of adaptive traffic signal control systems. In the past few years, vehicle trajectory data has become increasingly available and offers many advantages over detectors and other infrastructure-based sensors for traffic monitoring. However, one major challenge of using vehicle trajectory data for traffic signal re-timing is the data sparsity and incompleteness caused by the limited penetration rate.

This dissertation aims at providing systematic methods for traffic signal optimization with vehicle trajectory data at the current market penetration rate (<10%). The main contribution is the newly proposed stochastic traffic flow model under Newellian coordinates, which is established based on Newell’s simplified car-following model. We show that a point-queue model under the Newellian coordinates can sufficiently capture the whole spatial-temporal traffic state through the PTS (probabilistic time-space) diagram. This simplification is made feasible by ignoring the stochastic driving behavior since most of the system uncertainty comes from the stochastic traffic demand as well as sparse observation at a low penetration rate.

The main advantage of the proposed model is that it is a stochastic model with much lower dimensions and can be directly calibrated by taking the vehicle trajectory data as the input. It enables us to apply different statistical estimation algorithms to estimate both stationary traffic parameters (i.e., penetration rate, average arrival rate, etc.) and real-time traffic state (queue length). Based on the estimated traffic state and parameters, we also develop different optimization programs for the re-timing of fixed-time traffic signals and a rule-based QCC (queue clearance control) for real-time traffic signals.

With the proposed methods, we develop an integrated traffic signal re-timing system called OSaaS (Optimizing traffic Signals as a Service). In April 2022, a citywide field test of OSaaS was conducted in Birmingham, Michigan, with 34 signalized intersections. 2 corridors and 2 isolated intersections were implemented with new fixed-time signal timing plans, resulting in decreases in both the delay and number of stops by up to 20% and 30%, respectively. OSaaS is a closed-loop iterative system including performance evaluation, traffic state estimation, traffic signal diagnosis, and optimization. By not requiring installation or maintenance of vehicle detectors, OSaaS provides a more scalable, sustainable, resilient, responsive, and efficient solution to traffic signal re-timing based on vehicle trajectory, which could be applied to every traffic signal in the world.