Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty

TL;DR

This paper introduces a novel distributed traffic signal control policy called CMPP, which improves traffic flow efficiency by coordinating intersections using pressure-based metrics and optimization algorithms, with proven stability and superior performance in simulations.

Contribution

The paper proposes the CMPP control policy that combines pressure-based metrics with coordination, providing stability guarantees and demonstrating improved performance over benchmarks in large-scale simulations.

Findings

CMPP guarantees network stability.

CMPP reduces average travel and waiting times.

CMPP outperforms benchmark controllers in simulations.

Abstract

This paper develops an adaptive traffic control policy inspired by Maximum Pressure (MP) while imposing coordination across intersections. The proposed Coordinated Maximum Pressure-plus-Penalty (CMPP) control policy features a local objective for each intersection that consists of the total pressure within the neighborhood and a penalty accounting for the queue capacities and continuous green time for certain movements. The corresponding control task is reformulated as a distributed optimization problem and solved via two customized algorithms: one based on the alternating direction method of multipliers (ADMM) and the other follows a greedy heuristic augmented with a majority vote. CMPP not only provides a theoretical guarantee of queuing network stability but also outperforms several benchmark controllers in simulations on a large-scale real traffic network with lower average travel and waiting time per vehicle, as well as less network congestion. Furthermore, CPMM with the greedy algorithm enjoys comparable computational efficiency as fully decentralized controllers without significantly compromising the control performance, which highlights its great potential for real-world deployment.