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
The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.
By Hanlin Sun, Jiayang Li
The paper proposes adding a social component—marginal cost—to autonomous vehicle (AV) routing rewards to account for the impact of AVs on human-driven vehicles (HDVs). Using multi-agent reinforcement learning on real-world networks, the authors show that this social reward reduces HDV travel time increases and improves overall system performance compared to purely selfish AV routing. A scalable approach limits counterfactual simulations to the most influential AVs, maintaining performance while cutting computational cost.
By Anastasia Psarou, Mikolaj Rams, Blazej Torbus, Rafal Kucharski
arXiv:2508. 04811v2 Announce Type: replace Abstract: Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience.
By Lin Jiang, Yu Yang, Guang Wang
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.
By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
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.
By Madelaine Martinez-Ferguson, Chun Wang, Mustafa Can Camur, Xueping Li