Quantifying Traffic at Highway-Rail Grade Crossings Using Artificial Intelligence

Rail-highway grade crossings remain a critical safety concern, with 2,246 incidents, 266 fatalities, and 744 injuries in 2024 based on data from the US Department of Transportation (USDOT) and the Federal Railroad Administration (FRA). There is a need to explore new standards and technologies that increase safety at grade crossings and eliminate incidents. Updated reports and data from crossings are necessary to achieve that goal. However, the number of crossings in the US is vast. Although the FRA maintains a crossing inventory as a publicly available resource, which has invaluable data for operation and maintenance, many crossings have outdated information. Analysis of the data in the crossing inventory reveals that the average age of Annual Average Daily Traffic (AADT) in revisions submitted in 2024 is approximately 16.5 years old. Also, many crossings are missing that information completely. As such, there is a need to update traffic information at rail crossings. Manually counting and classifying vehicles to estimate daily traffic is tedious and labor intensive. This research intends to address the need for updated traffic information at grade crossings using Artificial Intelligence (AI). The proposed work will extend on previous research by the team by implementing advanced AI models, using Deep Learning (DL), which will automatically detect, classify, and track vehicles and pedestrians at grade crossings using video streams from cameras. The developed model will capitalize on crowd-sourced and publicly available videos for model training and validation. The model will be able to use affordable cameras to be a cost-effective solution. The AADT information produced from the model will support decision-makers to prioritize safety updates at rail crossings.

• Record URL:
  • https://www.utrgv.edu/railwaysafety/research/operations/index.htm
• Supplemental Notes:
  • The National Transportation Safety Board (NTSB) is a partner for this project.

Language

• English

Project

• Status: Active
• Funding: $99,509.00
• Contract Numbers:

  69A3552348340

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  University of Texas Rio Grande Valley

  1201 W. University Dr
  Edinburg, TX  United States  78539
• Managing Organizations:

  University of Texas Rio Grande Valley

  1201 W. University Dr
  Edinburg, TX  United States  78539
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Texas Rio Grande Valley

  1201 W. University Dr
  Edinburg, TX  United States  78539
• Principal Investigators:

  Ali, Gasser

  Tarawneh, Constantine

• Start Date: 20250601
• Expected Completion Date: 20260831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Annual average daily traffic; Artificial intelligence; Automatic data collection systems; Deep learning; Highway safety; Railroad grade crossings; Railroad safety
• Subject Areas: Data and Information Technology; Highways; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01960673
• Record Type: Research project
• Source Agency: University Transportation Center for Railway Safety
• Contract Numbers: 69A3552348340
• Files: UTC, RIP
• Created Date: Jul 14 2025 7:08PM