Utilizing Traffic Signal Pedestrian Push-Button Data for Pedestrian Planning and Safety Analysis

Transportation planning, traffic monitoring, and traffic safety analysis require detailed information about pedestrian volumes, but such data are usually lacking. Fortunately, recent research has demonstrated the accuracy of pedestrian volumes estimated from push-button data contained within high-resolution traffic signal controller log data. Such data are available continuously for many locations. This project takes advantage of these novel pedestrian traffic signal data to advance pedestrian traffic monitoring and improve pedestrian traffic safety by applying them as estimates of volume and exposure, often alongside advanced machine learning techniques. Through a series of five studies, the authors identify temporal patterns in pedestrian activity; study the accuracy of pedestrian volume estimation methods over time; use machine learning methods to improve the quality and completeness of pedestrian time-series data; analyze crashes to identify a "safety in numbers" effect for pedestrians; and apply a new deep learning model to better understand factors affecting pedestrian crash severity. Altogether, this work leverages novel pedestrian traffic signal data to further research and efforts in pedestrian traffic monitoring and safety.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1168
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/75586
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Utah State University, Logan

  Department of Civil and Environmental Engineering
  Logan, UT  United States 

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

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

  Utah Department of Transportation

  Salt Lake City, UT  United States 
• Authors:
  • Singleton, Patrick
  • Rafe, Amir
  • Humagain, Prasanna
  • Runa, Ferdousy
  • Islam, Ahadul
  • Mekker, Michelle
• Publication Date: 2024-6

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Maps; References; Tables;
• Pagination: 106p

Subject/Index Terms

• TRT Terms: Crash severity; Data analysis; Machine learning; Pedestrian actuated controllers; Pedestrian safety; Pedestrian vehicle crashes; Traffic signal controllers; Traffic surveillance; Traffic volume
• Geographic Terms: Utah
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01923705
• Record Type: Publication
• Report/Paper Numbers: MPC-24-525
• Contract Numbers: MPC-622
• Files: UTC, NTL, TRIS, USDOT, STATEDOT
• Created Date: Jul 8 2024 9:08AM