A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System Data

In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system f...

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Bibliographic Details
Published in:2021 International Conference on Computational Science and Computational Intelligence (CSCI) pp. 285 - 289
Main Authors: Manyfield-Donald, Cheronika, Kwembe, Tor A., Cheng, Jing-Ru C.
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2021
Subjects:
Automatic Identification System
Big Data
Clustering algorithms
Clustering methods
Clustering Using Representatives (CURE)
Data collection
Machine learning
Machine learning algorithms
Scientific computing
Tracking
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Summary:In this paper, we introduce a modification of the Clustering Using Representatives (CURE) algorithm to enhance and optimize the tracking and monitoring of maritime traffic in real-time using the Automatic Identification System (AIS) data. In doing so, we present a 2-D points data collection system for the utilization of a modified unsupervised machine learning clustering method of the CURE algorithm integrated with a data streaming algorithm to develop a more efficient method that will directly assist in adverting accidents and support in vessels avoiding dangerous environments. Results are presented that show tracking in inland as well as open sea waterways.
DOI:10.1109/CSCI54926.2021.00119