Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections

TL;DR

This paper presents a distributed optimization framework for coordinating traffic lights and connected automated vehicles at intersections, improving traffic management across different CAV penetration levels.

Contribution

It introduces a novel penalization-enhanced algorithm to solve a complex mixed-integer quadratic program in a distributed manner for traffic control.

Findings

Effective coordination at various CAV penetration rates

Feasible person-by-person optimal solutions achieved

Validated through extensive simulations

Abstract

In this paper, we consider the problem of coordinating traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We aim to develop an optimization-based control framework that leverages both the coordination capabilities of CAVs at higher penetration rates and intelligent traffic management using traffic lights at lower penetration rates. Since the resulting optimization problem is a multi-agent mixed-integer quadratic program, we propose a penalization-enhanced maximum block improvement algorithm to solve the problem in a distributed manner. The proposed algorithm, under certain mild conditions, yields a feasible person-by-person optimal solution of the centralized problem. The performance of the control framework and the distributed algorithm is validated through simulations across various penetration rates and traffic volumes.