Distributed Urban Freeway Traffic Optimization Considering Congestion Propagation

TL;DR

This paper introduces a distributed traffic optimization method for urban freeways that considers congestion propagation effects, using historical data and advanced algorithms to improve traffic flow management.

Contribution

It presents a novel approach to identify potential congestion areas and integrates them into a distributed optimization framework using ADMM, enhancing scalability and effectiveness.

Findings

The proposed method effectively reduces congestion propagation.

It converges to the optimal solution under convex objectives.

Simulation results demonstrate improved traffic flow in Shanghai.

Abstract

Effective traffic optimization strategies can improve the performance of transportation networks significantly. Most exiting works develop traffic optimization strategies depending on the local traffic states of congested road segments, where the congestion propagation is neglected. This paper proposes a novel distributed traffic optimization method for urban freeways considering the potential congested road segments, which are called potential-homogeneous-area. The proposed approach is based on the intuition that the evolution of congestion may affect the neighbor segments due to the mobility of traffic flow. We identify potential-homogeneous-area by applying our proposed temporal-spatial lambda-connectedness method using historical traffic data. Further, global dynamic capacity constraint of this area is integrated with cell transmission model (CTM) in the traffic optimization problem. To reduce computational complexity and improve scalability, we propose a fully distributed algorithm to solve the problem, which is based on the partial augmented Lagrangian and dual-consensus alternating direction method of multipliers (ADMM). By this means, distributed coordination of ramp metering and variable speed limit control is achieved. We prove that the proposed algorithm converges to the optimal solution so long as the traffic optimization objective is convex. The performance of the proposed method is evaluated by macroscopic simulation using real data of Shanghai, China.