DVS-RG: Differential Variable Speed Limits Control using Deep Reinforcement Learning with Graph State Representation
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
Variable speed limit (VSL) control is an established yet challenging problem to improve freeway traffic mobility and alleviate bottlenecks by customizing speed limits at proper locations based on traffic conditions. Recent advances in deep reinforcement learning (DRL) have shown promising results in solving VSL control problems by interacting with sophisticated environments. However, the modeling of these methods ignores the inherent graph structure of the traffic state which can be a key factor for more efficient VSL control. Graph structure can not only capture the static spatial feature but also the dynamic temporal features of traffic. Therefore, we propose the DVS-RG: DRL-based differential variable speed limit controller with graph state representation. DVS-RG provides distinct speed limits per lane in different locations dynamically. The road network topology and traffic information(e.g., occupancy, speed) are integrated as the state space of DVS-RG so that the spatial features can be learned. The normalization reward which combines efficiency and safety is used to train the VSL controller to avoid excessive inefficiencies or low safety. The results obtained from the simulation study on SUMO show that DRL-RG achieves higher traffic efficiency (the average waiting time reduced to 68.44%) and improves the safety measures (the number of potential collision reduced by 15.93% ) compared to state-of-the-art DRL methods.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 37 references that are in the map. First posted on arXiv 15 May 2024.
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7 of 37 references.
References · 7
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2020IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Controlcited by 22
- 2022Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersectionscited by 16
- 2022CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learningcited by 6
- 2013Control of a lane-drop bottleneck through variable speed limitscited by 1
- 2018Feedback Control of Scalar Conservation Laws with Application to Density Control in Freeways by Means of Variable Speed Limitscited by 1
- 2021Selection of the Speed Command Distance for Improved Performance of a Rule-Based VSL and Lane Change Controlcited by 1
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- 2026Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments
- 2024Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing
- 2025Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
- 2026Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
- 2022Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing
- 2026Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
- 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark