Multi-Agent Deep Deterministic Policy Gradient-Based Coordinated Control for Urban Expressway Entrance–Arterial Interfaces

Coordinated control of ramp metering, variable speed limits, and intersection signals is critical for mitigating congestion and enhancing efficiency at urban expressway–arterial interfaces. Existing strategies often operate in isolation, leading to fragmented responses and limited adaptability under heterogeneous traffic demands. This study develops a multi-agent reinforcement learning framework based on MADDPG to achieve cooperative decision-making across heterogeneous controllers. An asynchronous control cycle mechanism is designed to accommodate different temporal requirements of ramp meters, speed limits, and signal controllers, ensuring practical feasibility in real-time operations. A conflict-aware reward design further embeds density regulation, speed harmonization, and spillback prevention to stabilize flow dynamics. Simulation experiments on a calibrated urban network demonstrate that the proposed framework delays congestion onset, reduces shockwave propagation, and improves throughput compared with classical benchmarks. In particular, at the mainline merge, average travel time is reduced to 13.56 s (62.4% of VSL-only); at the ramp, occupancy is lowered to 6.4% (40.6% of ALINEA); and at the signalized approach, average delay decreases to 85.71 s (62.7% of actuated control). These results highlight the scalability and deployment potential of the proposed cooperative control approach for system-level traffic management in mixed traffic environments.