Network-wide critical routes identification and coordinated control based on automatic vehicle identification data

Coordinated signal control is an effective way to improve traffic efficiency in urban road networks. This study presents a network-wide coordinated signal control method based on the identification of critical routes and the partitioning of coordinated intersection groups using automatic vehicle identification (AVI) data. A movement-level network representation model is first proposed to identify critical routes by analyzing the fully sampled paths of individual vehicles extracted from the network's AVI data. Then, the optimization of the entire road network is divided into multiple coordinated intersection groups and potential isolated intersections by minimizing total delay, accounting for the interactions between cycle length and delay at intersections along all critical routes. Based on the ring-and-barrier scheme, the signal control problem for each coordinated intersection group is formulated as a mixed integer linear programming (MILP) model aimed at maximizing the bandwidth allocated to critical route bands. Utilizing real AVI data collected from the network of Baoding City, China, the experimental results demonstrate that the proposed partitioning method outperforms static clustering methods that rely on traffic state, e.g., density, for control purposes, particularly in coordinated intersection groups. In comparison to state-of-the-art studies, the analyses indicate that the comprehensive framework, which ranges from critical routes identification and network partition to traffic signal control based on AVI data, significantly enhances network performance by reducing the queue length and average delay at intersections.