11 Jun 2025

Prediction of Waterborne Freight Activity with Automatic Identification System Using Machine Learning

Abstract: This paper addresses latency issues related to publicly available port-level commodity tonnage reports. Predicting commodity tonnage at the port-level, near real time vessel tracking data is used with historical WCS with a machine learning model. Commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. This study leverages near real time vessel tracking data from the AIS data set. LSTM, TCN, and TFT machine learning models are developed using the features extracted from AIS and the historical WCS data. The output of the model is the prediction of the quarterly volume of commodities at port terminals for four quarters in the future. Uncategorized and Categorized models were developed. The uncategorized outperformed the categorized based on the Mean Absolute Percentage Error. The uncategorized LSTM model has the highest accuracy. Results show the model has higher accuracy for port terminals that handle a specific type of vessel, compared to the port terminals handling more than one vessel type. The application of the model enables port authorities and stakeholders to make short-term capacity expansion and infrastructure investment decisions based on commodity volume.