This study presents a machine learning approach to predict the number of barges transported by vessels on inland waterways using tracking data from the Automatic Identification System (AIS). While AIS tracks the location of tug and tow vessels, it does not monitor the presence or number of barges transported by those vessels. Understanding the number and types of barges conveyed along river segments, between ports, and at ports is crucial for estimating the quantities of freight transported on the nation's waterways. This insight is also valuable for waterway management and infrastructure operations impacting areas such as targeted dredging operations, and data-driven resource allocation. Labeled sample data was generated using observations from traffic cameras located along key river segments and matched to AIS data records. A sample of 164 vessels representing up to 42 barge convoys per vessel was used for model development. The methodology involved first predicting barge presence and then predicting barge quantity. Features derived from the AIS data included speed measures, vessel characteristics, turning measures, and interaction terms. For predicting barge presence, the AdaBoost model achieved an F1 score of 0.932. For predicting barge quantity, the Random Forest combined with an AdaBoost ensemble model achieved an F1 score of 0.886. Bayesian optimization was used for hyperparameter tuning. By advancing predictive modeling for inland waterways, this study offers valuable insights for transportation planners and organizations, which require detailed knowledge of traffic volumes, including the flow of commodities, their destinations, and the tonnage moving in and out of ports.

翻译：本研究提出了一种机器学习方法，利用自动识别系统（AIS）的跟踪数据预测内河航道船舶运输的驳船数量。虽然AIS能够追踪拖轮和推轮的位置，但无法监测这些船舶所运输驳船的存在与否及其数量。了解河段、港口之间以及港口内运输的驳船数量和类型，对于估算国家水路货运量至关重要。这一认知对于航道管理和基础设施运营（例如针对性疏浚作业和数据驱动的资源分配）也具有重要意义。本研究利用关键河段沿线交通摄像头的观测数据生成标记样本，并将其与AIS数据记录进行匹配。模型开发使用了包含164艘船舶的样本，每艘船舶最多代表42个驳船船队。方法学首先预测驳船存在性，进而预测驳船数量。从AIS数据中提取的特征包括速度指标、船舶特征、转向指标以及交互项。在驳船存在性预测中，AdaBoost模型的F1分数达到0.932。在驳船数量预测中，随机森林结合AdaBoost集成模型的F1分数达到0.886。研究采用贝叶斯优化进行超参数调优。通过推进内河航道的预测建模，本研究为交通规划机构和相关组织提供了重要参考，这些机构需要掌握包括货物流向、目的地及港口吞吐量在内的详细交通流量信息。