Toward a Marine Road Network for Ship Passage Planning and Monitoring

Sean M Kohlbrenner,Christos Kastrisios,Nilan T Phommachanh,Amith Kashyap,Val Schmidt,Matthew K Eager

doi:10.5194/ica-proc-4-61-2021

Abstract

Abstract. Safety of navigation is essential for the global economy as maritime trade accounts for more than 80% of international trade. Carrying goods by ship is economically and environmentally efficient, however, a maritime accident can cause harm to the environment and local economies. To ensure safe passage, mariners tend to use already familiar routes as a best practice; most groundings occur when a vessel travels in unfamiliar territories or suddenly changes its route, e.g., due to extreme weather. In highly trafficked areas, the highest risk for ships is that of collision with other vessels in the area. In these situations, a network of previously traversed routes could help mariners make informed decisions for finding safe alternative routes to the destination, whereas a system that can predict the routes of nearby vessels would ease the burden for the mariner and alleviate the risk of collision. The goal of this project is to utilize Automatic Identification System data to create a network of “roads” to promote a route planning and prediction system for ships that makes finding optimal routes easier and allows mariners on the bridge and Autonomous Surface Vehicles to predict movement of ships to avoid collisions. This paper presents the first steps taken toward this goal, including data processing through the usage of Python libraries, database design and development utilizing PostgreSQL, density map generation and visualizations through our own developed libraries, an A* pathfinding algorithm implementation, and an early implementation of an Amazon Web Services deployment.

Highlights

The Automatic Identification System (AIS) is a maritime transceiver system developed to provide ship identification and positioning information to other vessels and shore stations
AIS improves situational awareness of vessels in vicinity and assists mariners in safely navigating their vessels, while historic AIS data provides a means for studying maritime traffic related issues
The Roads Of The Sea (ROTS) project (Kastrisios et al, 2021) aims to fill this gap by harvesting AIS information and chart data into a streamlined route suggestion and route prediction system with the goal to mitigate the dangers of traversing unverified routes and highly congested areas

Summary

Introduction

The Automatic Identification System (AIS) is a maritime transceiver system developed to provide ship identification and positioning information to other vessels and shore stations. AIS improves situational awareness of vessels in vicinity and assists mariners in safely navigating their vessels, while historic AIS data provides a means for studying maritime traffic related issues. Aggregated AIS data is illustrative of global ship traffic; many research works have studied methods for deriving usable products from historic AIS data with creating class-specific heat maps of traffic (e.g., Falco et al, 2019), interpolating ship positions where data is missing (e.g., Mao et al, 2018), predicting ship trajectories (e.g., Liu et al, 2019), and extracting predominant routes in grid or vector format (e.g., Guyader et al, 2011; Filipiak et al, 2020), for applications such as identifying anomalous ship behavior (e.g., Zissis et al, 2020), mapping fishing efforts (e.g., Natale et al, 2015), establishing hierarchically related statistical models to simulate traffic and assess navigation risk (e.g., Calder & Schwehr, 2009), assessing shipping energy efficiency (Smith et al, 2013), creating new traffic safety corridors (e.g., ACPARS, 2016), and enhancing cetaceans-ship collision avoidance (e.g., McGillivary et al, 2009). We discuss the first phase of this developing project which includes data discovery and filtration, and the development of a grid system for storing the aggregated AIS data, path finding algorithm, and the deployment of a preliminary Amazon Web Services interface

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