A Review of Research on Coordinated Control of Traffic Signals at Urban Road Intersection Groups

Effective traffic signal coordination is essential for urban intersection groups, helping reduce delays and improve throughput efficiency. This paper systematically reviews the progress in intersection group signal coordination control, focusing on four main aspects. First, an overview of partitioning methods is provided from both static and dynamic perspectives. Next, optimisation-based signal coordination is classified into two main approaches: single-objective and multi-objective control. We then present advanced adaptive signal control strategies, with a focus on deep reinforcement learning techniques. Finally, signal coordination in intelligent and connected environments is explored, addressing three key scenarios: trunk roads, road networks and non-signalised intersections. The research shows that intersection group partitioning is moving toward dynamic and multi-criteria approaches. Signal coordination is shifting toward multi-objective optimisation and proactive adaptive control to address complex traffic environments. Deep reinforcement learning, particularly deep Q-networks and its variants, has been widely applied in adaptive signal control for real-time traffic flow adjustments. In intelligent and connected environments, the collaborative optimisation between intersections is a key research focus. This paper provides a theoretical framework for intersection group signal coordination, with broad applications in improving traffic efficiency, reducing congestion and advancing intelligent transportation systems.