Coordinated lane speed limits and ramp metering cut traffic risks

Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach

Machine Learning

Summary

Traffic bottlenecks where cars frequently change lanes cause jams and crashes on city highways. The authors created a combined method that adjusts lane speed limits and controls highway entrances to reduce these problems. Their approach uses a detailed traffic model and smart AI agents that learn and adapt to traffic changes. Tests on a real 18-kilometer highway showed their method predicts traffic well and improves safety and flow better than earlier techniques. This approach could help keep urban highways moving more smoothly and safely.

What this means in practice

  • For urban traffic management teams: Coordinate lane-specific speed limits and ramp signals on congested urban expressways to reduce accidents and improve traffic flow.
  • For highway operations engineers: Use long- and short-term control layers combining models and AI to optimize traffic signals and speed limits on complex highway sections.

Authors

Guodong Ma, Baofeng Sun, Wenyu Yang, Zhihong Yao

Abstract

Successive weaving segments (SWSs) on urban expressways are bottlenecks prone to recurrent congestion and collisions, requiring fine-grained active traffic management (ATM). Existing approaches struggle to balance the adaptive performance of data-driven optimization with the resilience and transferability of model-based control. We propose a hybrid framework to coordinate lane-level variable speed limits (VSLs) and ramp metering across SWSs. First, we reconstruct L-METANET, a lane-level macroscopic traffic flow model that captures free and forced lane changes. Second, we combine XGBoost-SHAP with a random-parameters binary logit (RPBL) model to derive analytical equations for merging and diverging collision risks and formulate system cost and reward functions. Third, we develop MPC-STMAPPO, a hierarchical controller integrating model predictive control (MPC) and multi-agent reinforcement learning (MARL). Its upper MPC layer uses L-METANET for long-horizon rolling optimization and generates baseline commands; its lower spatiotemporal MAPPO (ST-MAPPO) layer, enhanced with Mamba cells and graph attention, produces residual actions for short-horizon adjustment. Real-world experiments on the 18-km Eastern Expressway in Changchun, China, show that L-METANET accurately reproduces lane-changing-induced flow redistribution and capacity drops, with state evolution aligned with ground truth. XGBoost-SHAP-RPBL achieves AUCs above 0.80 in most tasks, outperforming conventional logit models. MPC-STMAPPO converges faster and performs better across multiple metrics than MPC- and MARL-based baselines. Under randomly fluctuating demand, it also significantly outperforms pure MARL in generalization, demonstrating strong potential for industrial deployment.
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