Monitoring and evaluation of ship operation congestion status at container ports based on AIS data

Abstract

With the rapid development of the shipping industry, many factors, such as larger ships, harsh weather, workers' strikes, surging port throughputs, and the global pandemic, have made port congestion frequent worldwide. How to effectively monitor and evaluate the ship operation congestion status at ports becomes a key challenge for global ports to seek sustainable development. Based on Automatic Identification System (AIS) data, this article analyzed the semantics of ship trajectory by using data mining methods such as the sliding window algorithm, explored ship berthing and berth-waiting operation events, and established models for calculating the congestion index at ports, as well as the time cost, capacity resource cost, and economic cost caused by ship delays to monitor and evaluate the ship operation congestion status at ports. The results showed that the monitoring and evaluation system for container ports based on AIS ship data can effectively monitor and evaluate the port congestion status and support the decision-making efforts of all parties on the supply chain, such as ports, ship enterprises, cargo owners, and shipping agents.