Traffic Signal Control with Connected Vehicles

Abstract

The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors, often in-ground inductive loop sensors, can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field due to their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles would allow for the wireless transmission of vehicles’ positions, headings, and speeds to be used by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm (PMSA), was developed in this research to utilize these new, more robust data. The decentralized, fully adaptive traffic control algorithm uses a rolling horizon strategy, where the phasing is chosen to optimize an objective function over a 15-second period in the future. The objective function uses either delay-only, or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a microscopic simulation driven by present vehicle positions, headings, and speeds. Unlike most adaptive control strategies, the algorithm is relatively simple, does not require point detectors or signal-to-signal communication, and is completely responsive to immediate vehicle demands. To ensure drivers’ privacy, the algorithm stores no memory of individual or aggregate vehicle locations. Results from simulation show that the algorithm maintains or improves performance compared to a state-of-practice coordinated-actuated timing plan optimized by Synchro at low- and mid-level volumes, but performance worsens during saturated and oversaturated conditions. Testing also showed improved performance during periods of unexpected high demand and the ability to automatically respond to year-to-year growth without retiming.