Novel Big Data and Artificial Intelligence Analytics Methods for Tracking and Monitoring Maritime Traffic

This project will utilize Automatic Identification System (AIS) datasets to design scalable Maritime Traffic Monitoring and Analysis (MTMA) applications and tools and work with two Computational data enabled science and engineering (CDS&E) students produce two dissertations in this direction. Critical applications such as the detection of anomalies, offshore and onshore attacks and data intrusions, require fast mechanisms for Artificial Intelligence (Al) analysis of thousands of events per second, as well as efficient techniques for the analysis of massive historical AIS data. There has been major developments of Big Data Analysis Frameworks for analyzing the AIS historical data, but their applications and scalable analysis techniques to the MTMA domain remains poorly understood and difficult to benchmark. This project introduces several novel 2-D points data collection system using AIS data that will aid in monitoring maritime traffic and directly assist in averting accidents, tracking vessels, and support in avoidance of dangerous environments. Density-based spatial clustering of Applications (DBSCAN) using pairwise distance matrices, the Haversine distance function and real-time AIS streaming data algorithms are a few of the many techniques we will employ finding core points in relation to a vessel and its outliers. This will show AIS equipped vessels/ objects in an inputted radius to a given Latitude/Longitude coordinate pair and identifies anomalies or what could possibly be other AIS equipped vessels/object with erratic behavior using streaming AIS data. The project also explored a distributed cloud-computing framework based on the Big Data and Artificial Intelligent data analytics approach where both storage and computing resources can be scaled out to collect and process marine vessel traffic from AIS network in a reasonable time.

Language

• English

Project

• Status: Active
• Funding: $82500
• Contract Numbers:

  69A3551747130

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Maritime Transportation Research and Education Center

  University of Arkansas
  Fayetteville, AR  United States  72701
• Performing Organizations:

  Jackson State University, Jackson

  Department of Civil and Environmental Engineering
  Jackson, MS  United States  39217-0168
• Principal Investigators:

  Whalin, Robert

• Start Date: 20211101
• Expected Completion Date: 20230630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data collection; Detection and identification systems; Maritime safety; Monitoring; Water traffic
• Identifier Terms: National Automatic Identification System
• Subject Areas: Data and Information Technology; Marine Transportation; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01790427
• Record Type: Research project
• Source Agency: Maritime Transportation Research and Education Center
• Contract Numbers: 69A3551747130
• Files: UTC, RIP
• Created Date: Dec 6 2021 2:09PM