用逆强化学习分析船舶靠泊行为，预测港口拥堵与停靠时间。
Temporal-IRL: Modeling Port Congestion and Berth Scheduling with Inverse Reinforcement Learning
- 基于AIS数据重构靠泊计划，通过逆强化学习推导调度奖励函数。
- 在纽约/新泽西港马赫尔码头数据上，准确预测船舶靠泊顺序和总停留时间。
- 适用于港口运营优化、供应链调度与智能物流系统设计者。
准确预测港口拥堵对保障全球供应链稳定至关重要。本研究聚焦船舶在特定码头的靠泊调度行为，通过分析历史自动识别系统（AIS）位置数据，重建靠泊计划，并利用逆强化学习（IRL）推导出反映调度优先级的奖励函数。针对纽约/新泽西港马赫尔码头（Maher Terminal），构建了Temporal-IRL模型，该模型能有效预测船舶靠泊序列及总停留时间（含等待与靠泊时长），从而实现港口拥堵的精准预测。基于2015年1月至2023年9月的数据训练与测试，模型表现显著优异。
原文摘要 · Abstract (English)
Predicting port congestion is crucial for maintaining reliable global supply chains. Accurate forecasts enableimprovedshipment planning, reducedelaysand costs, and optimizeinventoryanddistributionstrategies, thereby ensuring timely deliveries and enhancing supply chain resilience. To achieve accurate predictions, analyzing vessel behavior and their stay times at specific port terminals is essential, focusing particularly on berth scheduling under various conditions. Crucially, the model must capture and learn the underlying priorities and patterns of berth scheduling. Berth scheduling and planning are influenced by a range of factors, including incoming vessel size, waiting times, and the status of vessels within the port terminal. By observing historical Automatic Identification System (AIS) positions of vessels, we reconstruct berth schedules, which are subsequently utilized to determine the reward function via Inverse Reinforcement Learning (IRL). For this purpose, we modeled a specific terminal at the Port of New York/New Jersey and developed Temporal-IRL. This Temporal-IRL model learns berth scheduling to predict vessel sequencing at the terminal and estimate vessel port stay, encompassing both waiting and berthing times, to forecast port congestion. Utilizing data from Maher Terminal spanning January 2015 to September 2023, we trained and tested the model, achieving demonstrably excellent results.
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