Traffic Signal Control with Connected Vehicles

Goodall, Noah J.; Smith, Brian L.; Park, Byungkyu · 2013 · OpenAlex-citations

DOI: 10.3141/2381-08

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Summary

This paper addresses the limitations of traditional traffic signal control systems, which rely on point detectors like inductive loops that provide only limited, fixed-location data. The authors argue that existing adaptive control strategies are often too complex for widespread implementation and fail to capture comprehensive vehicle dynamics. Motivated by the emergence of connected vehicle technology, which enables wireless transmission of vehicle positions, headings, and speeds, the study proposes a new decentralized, fully adaptive traffic control algorithm called the Predictive Microscopic Simulation Algorithm (PMSA). The primary goals of PMSA are to match or exceed the performance of state-of-practice coordinated-actuated systems, respond to real-time demands without manual retiming, and protect driver privacy by avoiding the storage or tracking of individual vehicle data. The PMSA utilizes a rolling horizon strategy to optimize an objective function over a 15-second future period. Instead of relying on point detectors or signal-to-signal communication, the algorithm collects instantaneous snapshots of equipped vehicles within 300 meters of an intersection. It then uses microscopic simulation to predict vehicle behavior under various phasing scenarios, selecting the phase that minimizes the objective function. The study tested the algorithm using VISSIM simulation software on a calibrated model of four intersections along Route 50 in Chantilly, Virginia. The PMSA was compared against a Synchro-optimized coordinated-actuated timing plan across various scenarios, including different connected vehicle penetration rates (10%, 25%, 50%, and 100%), unexpected volume increases, annual traffic growth, and varying saturation rates. The objective function was tested using delay alone and combinations of delay, stops, and deceleration. Results indicate that PMSA maintains or improves performance compared to the baseline system at low and mid-level volumes, particularly when connected vehicle penetration rates are 50% or higher. At these levels, the algorithm significantly reduced delay and stopped delay while increasing average speed. The PMSA demonstrated superior adaptability during unexpected high demand, such as a 30% volume increase simulating incident rerouting, and automatically accommodated year-to-year traffic growth without requiring retiming. However, performance worsened under saturated and oversaturated conditions (saturation rates near 0.90), where the algorithm produced more stops than the coordinated-actuated system. Additionally, testing multivariable objective functions revealed that incorporating deceleration or stops did not significantly improve performance over a delay-only function; in fact, high acceleration factors degraded performance. The significance of this research lies in demonstrating a viable, privacy-preserving method for leveraging connected vehicle data for traffic control. The PMSA offers a simpler, decentralized alternative to complex adaptive systems, capable of responding instantly to real-time conditions and unexpected disruptions. While effective under unsaturated conditions, the findings highlight the need for future work to improve performance at low penetration rates and near-saturated conditions, potentially by estimating the positions of unequipped vehicles. This study provides a foundational approach for integrating connected vehicle technologies into traffic signal control to enhance efficiency and reduce congestion.

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• traffic density