# Maritime traffic congestion identification and ship trajectory prediction using temporal graph convolutional networks

## TL;DR

This paper introduces a new AI model to predict ship movements and detect traffic jams in busy maritime areas using real-world data.

## Contribution

A novel Temporal Graph Convolutional Network (T-GCN) model combining GCN and GRU for ship trajectory prediction and congestion identification.

## Key findings

- The T-GCN model effectively captures spatial and temporal patterns in maritime traffic.
- The proposed congestion measurement using SPI improves accuracy in identifying traffic hotspots.
- Experimental results confirm the model's effectiveness in real-world maritime scenarios.

## Abstract

With the rapid growth of global maritime trade, the efficient and safe management of maritime traffic has become increasingly critical. This study proposes a comprehensive framework for ship trajectory prediction and maritime traffic congestion identification based on Automatic Identification System (AIS) data. We integrate spatiotemporal analysis with deep learning techniques, specifically combining Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) to form a Temporal Graph Convolutional Network (T-GCN) model. This model effectively captures both spatial dependencies among ships and temporal dynamics in traffic flow. Furthermore, we introduce a congestion measurement indicator based on the Speed Performance Index (SPI) to quantify and identify congestion levels in maritime routes. The proposed method not only enhances the accuracy of ship trajectory prediction but also enables proactive congestion warnings, contributing to improved maritime safety and operational efficiency. Experimental results demonstrate the effectiveness of our approach in real-world scenarios.