Problem
Research questions and friction points this paper is trying to address.
Smart Freight Corridors
Agent-Based Models
Reinforcement Learning
Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning
🤖 AI Summary
本文使用基于代理的模型和强化学习解决智能货运走廊中自适应行为捕捉问题，通过三种情景评估表明认知情景在吞吐量和拥堵度方面表现更优。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agent-based Modeling (ABM)
Reinforcement Learning (RL)
Multi-Agent Reinforcement Learning (MARL)
Platoon Formation
Charging Coordination
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