← Back to all news
arXiv Machine Learning By Madelaine Martinez-Ferguson, Chun Wang, Mustafa Can Camur, Xueping Li

Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces an agent‑based modeling framework that integrates a physical infrastructure layer, a V2X connectivity layer, and a decision layer using reinforcement learning and multi‑agent reinforcement learning to simulate smart freight corridors. Three scenarios—Baseline, Assisted, and Cognitive—are evaluated on throughput, congestion, energy, emissions, and robustness, with the Cognitive scenario outperforming the baseline in throughput and congestion, and the Assisted scenario achieving energy savings via platooning. Sensitivity analysis shows that the smart corridor’s throughput advantage grows under high demand and that MARL coordination better utilizes fixed charging capacity than rule‑based methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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.

By Xavier Rate, Eloann Le Guern, Rapha\"el F\'eraud, Fatma Salem, Melissa Chiknoun, Eymeric Giabicani, Mehdi Feki, Patrick Maill\'e, Guy Camilleri, Anne Blavette, Hamid Benhamed
agentsreinforcement-learning
More like this →
arXiv AI
Aug 28

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.

By Biao Yin, Abderrahmane Kasmi, Nadir Farhi
agentsreinforcement-learningsafety
More like this →
Hugging Face Trending Papers
Aug 27

Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic

The paper presents a modeling and simulation framework to study reinforcement‑learning control of connected and automated vehicle (CAV) platoon joining maneuvers in mixed traffic. It evaluates Deep Q‑Network, Double Deep Q‑Network, and Proximal Policy Optimization algorithms, finding that PPO achieves a 98 % joining success rate with less than 1 % collisions by incorporating risk penalties, though it requires more decision steps. An external safety controller can prevent collisions but may reduce joining efficiency, highlighting a trade‑off between safety, effectiveness, and decision speed.

agentsreinforcement-learningsafety
More like this →
arXiv Machine Learning
2d ago

Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility

The paper proposes a multi‑agent reinforcement learning framework that dynamically allocates incentives to drivers, encouraging them to take alternative routes. By doing so, it reduces total travel time by up to 16% and CO₂ emissions by 9% under an unlimited budget, while revealing a trade‑off between the two objectives. The study demonstrates that a carefully weighted multi‑objective function can improve both travel time and emissions beyond baseline levels.

By Germ\'an Pardo-Gonz\'alez, Shaghayegh Vosough, Katerina Papadaki, Claudio Roncoli
agentsreinforcement-learning
More like this →