aiqbench analysis
This research applies multi-agent reinforcement learning and agent-based modeling to optimize smart freight corridors, demonstrating significant gains in throughput and energy efficiency over rule-based baselines. By simulating adaptive behaviors for connected and automated vehicles, the study provides a scalable framework for managing complex logistics networks without costly physical experimentation. The findings highlight how advanced ML techniques can solve real-world coordination problems in high-demand transport scenarios.
Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning
arXiv:2608.25193v1 Announce Type: cross Abstract: Smart freight corridors offer a practical pathway for connected and automated vehicle (CAV) deployment in freight transportation, but physical experimentation is expensive and existing approaches rely on predefined control policies that cannot capture adaptive behaviors. This paper presents an agent-based modeling (ABM) framework coupling a physical infrastructure layer, a connectivity layer (V2X), and a decision layer integrating reinforcement learning (RL) and multi-agent reinforcement learning (MARL) for platoon formation and charging coordination. We evaluate three scenarios (Baseline, Assisted, and Cognitive) using throughput, congestion, energy, emissions, and robustness metrics. Preliminary results indicate that the Cognitive scenario achieves higher throughput and lower congestion than the baseline, while the Assisted scenario delivers meaningful energy savings per kilometer through platooning. Sensitivity analysis shows that the throughput advantage of the smart corridor widens under conditions with high demand and that MARL coordination extracts greater utilization from fixed charging capacity than rule-based assignment.
Abstract:Smart freight corridors offer a practical pathway for connected and automated vehicle (CAV) deployment in freight transportation, but physical experimentation is expensive and existing approaches rely on predefined control policies that cannot capture adaptive behaviors. This paper presents an agent-based modeling (ABM) framework coupling a physical infrastructure layer, a connectivity layer (V2X), and a decision layer integrating reinforcement learning (RL) and multi-agent reinforcement learning (MARL) for platoon formation and charging coordination. We evaluate three scenarios (Baseline, Assisted, and Cognitive) using throughput, congestion, energy, emissions, and robustness metrics. Preliminary results indicate that the Cognitive scenario achieves higher throughput and lower congestion than the baseline, while the Assisted scenario delivers meaningful energy savings per kilometer through platooning. Sensitivity analysis shows that the throughput advantage of the smart corridor widens under conditions with high demand and that MARL coordination extracts greater utilization from fixed charging capacity than rule-based assignment.
Emerging Technologies (cs.ET); Machine Learning (cs.LG)
arXiv:2608.25193 [cs.ET]
(or arXiv:2608.25193v1 [cs.ET] for this version)
https://doi.org/10.48550/arXiv.2608.25193 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 243 words
