arXiv Machine Learning
Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning
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arXiv AI
Jul 1
Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.
arXiv AI
2d ago
Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
The paper presents a modeling and simulation framework to study reinforcement learning (RL) control of connected and automated vehicle (CAV) platoon joining maneuvers in mixed traffic. Using SUMO and agent-based modeling, it evaluates Deep Q-Network (DQN), Double DQN (DDQN), and Proximal Policy Optimization (PPO) algorithms, finding that PPO achieves a 98 % joining success rate with less than 1 % collision rate by incorporating risk penalties. The study also shows a trade‑off between safety, joining effectiveness, and decision efficiency, and demonstrates that an external safety controller can prevent collisions but may reduce joining efficiency.
arXiv AI
Jun 2
Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic
arXiv:2601. 18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles.
arXiv AI
Jul 28
Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Traffic
arXiv:2601. 11809v2 Announce Type: replace Abstract: Connected automated vehicles (CAVs) possess the ability to communicate and coordinate with one another, enabling cooperative platooning that enhances both energy efficiency and traffic flow.
arXiv AI
Jun 2
TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
arXiv:2604. 17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, yet extending them to physically grounded systems remains challenging.
arXiv AI
Jul 28
HiLLTS: Zero-Shot Hierarchical LLM-Guided Traffic Signal Control for Sustainable Transportation
arXiv:2607. 22691v1 Announce Type: new Abstract: Urban traffic congestion significantly increases fuel consumption, greenhouse gas emissions, and commuter delays, resulting in substantial economic losses and environmental harm in modern cities.