Artificial Intelligence-Aided Grade Crossing Safety Violation Detection Methodology and a Case Study in New Jersey

Fatalities at grade crossings accounted for an average of 33% of all railroad industry fatalities occurring in the past 10 years. As road traffic increases and high-speed rail deployments become more common in the United States, the number of fatalities is expected to remain a concern. Railroads have tackled this challenge through a combination of engineering, education, and enforcement campaigns. One of these efforts has been the increased deployment of security cameras throughout railroad networks. These video sources allow for the collection of big data to better understand grade crossing violation behaviors. However, monitoring these video feeds and extracting useful information requires prohibitive amounts of manual labor. This research utilizes state-of-the-art vision-based artificial intelligence (AI) techniques to record, recognize, and understand railroad video data in real time. This system’s understanding of active grade crossing violations helps to develop precise long-term grade crossing violation prevention strategies. This study explains how this AI-aided algorithm is used to monitor 1 year’s worth of violations at an active grade crossing in New Jersey and provides an overview of the observed trends. These data can be used to develop better engineering enforcement and education strategies for the mitigation of active grade crossing violations.

• Record URL:
  • https://doi.org/10.1177/03611981231163824
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Asim Zaman https://orcid.org/0000-0002-0117-2475© National Academy of Sciences: Transportation Research Board 2023.
• Authors:
  • Zaman, Asim
  • 0000-0002-0117-2475
  • Huang, Zhe
  • 0000-0002-8627-5122
  • Li, Weitian
  • 0000-0003-1835-8230
  • Qin, Huixiong
  • 0000-0002-9732-8832
  • Kang, Di
  • 0000-0003-3337-2815
  • Liu, Xiang
  • 0000-0002-4348-7432
• Publication Date: 2023-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 688-706
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2677
  • Issue Number: 10
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Artificial intelligence; Detection and identification systems; Highway safety; Railroad grade crossings; Traffic violations
• Geographic Terms: New Jersey
• Subject Areas: Highways; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01881970
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: May 11 2023 1:20PM