Journal cover Journal topic
Proceedings of the ICA
Journal topic
Articles | Volume 4
Proc. Int. Cartogr. Assoc., 4, 61, 2021
https://doi.org/10.5194/ica-proc-4-61-2021
Proc. Int. Cartogr. Assoc., 4, 61, 2021
https://doi.org/10.5194/ica-proc-4-61-2021

  03 Dec 2021

03 Dec 2021

Toward a Marine Road Network for Ship Passage Planning and Monitoring

Sean M. Kohlbrenner1,2, Matthew K. Eager1,2, Nilan T. Phommachanh1,2, Christos Kastrisios1, Val Schmidt1, and Amith Kashyap2 Sean M. Kohlbrenner et al.
  • 1University of New Hampshire, Center for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, Durham, NH, USA
  • 2University of New Hampshire, Department of Computer Science, Durham, NH, USA

Keywords: Roads of the Sea, Ocean Mapping, AIS, A*, Pathfinding, Autonomous Navigation, e-Navigation

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.

Publications Copernicus
Download
Citation