Community Detection in Shipping Network Based on AIS data

XinZhang, Yi Zuo, JunHao Jiang, Peng Jia

TL;DR: Proposing a method to analyze vessel traffic networks using AIS data for port authorities facing regulatory and safety challenges in congested waterways

Abstract: In response to the burgeoning shipping industry and its impact on congested waterways, port authorities face heightened regulatory pressures and navigation safety challenges. To aid these authorities, we propose a community discovery method for analyzing vessel traffic networks using AIS data. This method identifies stationary and turning points in vessel trajectories, applies feature-based clustering, extracts node attributes, and utilizes frequent trajectory pattern recognition to map waterway route networks. Through empirical analysis of cargo ship data from Ningbo-Zhoushan Port, our approach effectively identifies critical areas such as stationary and turning zones, aligning closely with real-world scenarios. This method offers port authorities timely insights into high-risk areas for safe navigation and supports optimized route planning, demonstrating its accuracy, effectiveness, and practicality.

Submission Number: 41