Identifying Intersection Groups for Traffic Signal Coordination in Urban Road Networks: A Network Partitioning Approach

Multi-intersection traffic signal coordination and control improves urban traffic efficiency by coordinating signal timing across suitable corridors or groups of intersections. However, most existing work focuses on optimizing the signal timing plan for predefined intersection groups such as all intersections on an arterial, whereas the question of which intersections should be coordinated together for maximum efficiency is often overlooked. Moreover, as traffic patterns vary throughout the day, the most suitable intersection groups may not be fixed across different Time-of-Day (ToD) demand conditions. To address this gap, this study proposes a network partitioning approach to adaptively identify effective groups of intersections for traffic signal coordination. A Signal Coordination Network (SCN) is constructed to quantify the coordination benefit between intersections based on traffic volume, spatial proximity, and cycle length compatibility. By solving a maximum set-packing problem, the proposed approach partitions the SCN into the most effective intersection groups, including isolated intersections, arterial progression groups, and network progression groups. Compared with the best-performing baselines, the proposed method reduces average travel time by 2.7% and average delay by 7.0% across the tested network–ToD scenarios, while the experiments under demand variation show statistically significant improvements in all AM and PM peak scenarios and comparable performance during off-peak periods. These results suggest that explicit coordination-group selection can provide additional operational benefits beyond local retiming, adaptive control, and predefined corridor or network coordination. The proposed framework offers a practical planning-level tool for designing ToD-sensitive signal coordination plans in urban networks.