A Hybrid Traffic Light Control Strategy Based on Branching Ratio Estimation and Congestion Identification

Abstract

Effective parameter estimation and low computational complexity are the two major challenges involved in traffic light control. Most traffic light scheduling strategies focus on developing well-tuned off-line solutions. This paper focuses on the design of a hybrid traffic light control strategy. A macroscopic traffic network model is proposed to depict the traffic dynamics and a closed-loop traffic control strategy is designed based on the estimation of branching ratios at intersections. To reduce the computational complexity, a distributed algorithm is proposed based on the congestion level identification and system partitioning method, which is based on machine learning algorithms. Simulation results show the effectiveness of the proposed methodologies.