Developing Artificial Intelligence Driven Safe Navigation Tool

Popular navigation applications such as Google Maps and Apple Maps provide distance-based or travel time-based alternative routes with no real-time risk scoring. There is a need for a real-time navigation system that can provide the data-driven decision on the safest path or route. By leveraging data from a diverse range of historical and real-time sources, this study successfully developed a user interface for a navigation tool or application that offers informed and data-driven decisions regarding the safest navigation options. The interface considers multiple scoring factors, including safety, distance, travel time, and an overall scoring metric. This study made a distinctive and valuable contribution by designing and implementing a robust safe navigation tool driven by artificial intelligence. Unlike existing navigation tools that offer multiple uninformed route options, this tool provides users with an informed decision on the safest route. By leveraging advanced AI algorithms and integrating various data sources, this navigation tool enhances the accuracy and reliability of route selection, thereby improving overall road safety and ensuring users can make informed decisions for their journeys.

• Dataset URL:
  • https://doi.org/10.15787/VTT1/AL4C8V
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/73307
• Dataset URL:
  • https://rosap.ntl.bts.gov/view/dot/73463
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Cover title: Developing AI-driven Safe Navigation Tool. Supporting dataset available at: https://rosap.ntl.bts.gov/view/dot/73463; https://doi.org/10.15787/VTT1/AL4C8V.
• Corporate Authors:

  Safety Through Disruption University Transportation Center (Safe-D)

  Texas A&M Transportation Institute
  College Station, TX  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Das, Subasish
  • Tsapakis, Ioannis
  • Weng, Yanmo
  • Torbic, Darren
  • Sohrabi, Soheil
  • Ye, Xinyue
  • Li, Shoujia
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 48p

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data analysis; Highway safety; Navigational aids; Risk assessment; Routes and routing
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01909630
• Record Type: Publication
• Report/Paper Numbers: 06-002
• Contract Numbers: 69A3551747115
• Files: UTC, NTL, TRIS, USDOT
• Created Date: Feb 23 2024 4:21PM