Preliminary Inter-comparison of AIS Data and Optimal Ship Tracks
Mannarini, Gianandrea; Carelli, Lorenzo; Zissis, Dimitris; Spiliopoulos, Giannis; Chatzikokolakis, Konstantinos
TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 1, doi:10.12716/1001.13.01.04, pp. 53-61, 2019
Abstract
Optimal ship tracks computed via the VISIRmodel are compared to tracks recorded by the Automatic Identification System (AIS). The evaluation regards 43 tracks in the Southern Atlantic Ocean, sailed during 2016-17 by different bulk carriers. In this exercise, VISIR is fed by wave analysis fields from the Copernicus Marine Environment Monitoring Service (CMEMS). In order to reproduce vessel speed loss in waves, a new methodology is developed, where kinematic information from AIS is complemented by wave information from CMEMS. Resulting VISIR tracks are analyzed along with AIS tracks in terms of their topological features and duration. The tracks exhibit quite diverse topological shapes, including orthodromic, loxodromic, and other paths with complex and dynamic diversions. The distribution of AIS to VISIR track durations is analyzed with respect to several parameters, such as the AIS to VISIR track length and their Fréchet distance. Model features of VISIR affecting the results are discussed and a future development suggested by the results are outlined.
This work has received funding from the European Union Horizon 2020 research and innovation programme under grant agreements No. 633211 (AtlantOS) and No. 732310 (BigDataOcean) and the Italy-Croatia Interreg V-A programme under project ID 10043587 (GUTTA).
.webp)
.webp)
Previous
Next
Navigating through dense waters: a toolbox for creating maritime density maps
A Big Data framework for Modelling and Simulating high-resolution hydrodynamic models in sea harbours
Modelling and simulating vessel emissions in real time based on terrestrial AIS data
Real-time maritime anomaly detection: detecting intentional AIS switch-off
A comparison of supervised learning schemes for the detection of search and rescue (SAR) vessel patterns
BigDataOcean Project: Early Anomaly Detection from Big Maritime Vessel Traffic Data
Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data
Determining the bilge water waste risk and management in the Gulf of Antalya by the Monte Carlo method
FleetMon Supports the Development of Environmental Impact Assessment on the Brazilian Coast
Scrapping Probabilities and Committed CO2 Emissions of the International Ship Fleet
Scalable In-Database Machine Learning for the Prediction of Port-to-Port Routes
How a real-time-based sea traffic forecast helps to organize and optimize the flow of maritime goods
Generating a node in an AIS-based routing graph for improved Estimated Time of Arrival. (Big) Data challenge: using AIS for generating a routing graph
Composition, spatial distribution and sources of macro-marine litter on the Gulf of Alicante seafloor (Spanish Mediterranean)
Climate change, non-indigenous species and shipping: assessing the risk of species introduction to a high-Arctic archipelago
The Big Picture: An Improved Method for Mapping Shipping Activities
GMSA: A Digital Twin Application for Maritime Route and Event Forecasting
Patterns of Life : Global Inventory for maritime mobility patterns
Require AIS data for your project?
Please reach out to us to discuss which Kpler offering will support your research project or academic study in the best possible and efficient way.
