Utilizing Archived Traffic Signal Performance Measures for Pedestrian Planning and Analysis

The overall goal of this project was to explore the use of continuous pedestrian traffic signal data from the Automated Traffic Signal Performance Measures (ATSPM) system to estimate pedestrian volumes at signalized intersections. The objectives were to identify patterns of pedestrian activity at signalized intersections, develop methods to estimate pedestrian crossing volumes from signal data, and create a prototype visualization. Using one year of data from 1,522 Utah traffic signals, the authors applied time series clustering across two new “pedestrian activity metrics” and identified seven distinct patterns of hourly and weekly pedestrian activity. These seven typologies varied by the magnitude (high, medium, and low) and the number (one or two) of weekday peak hours. Based on these typologies, the authors randomly selected 90 Utah signals, used Utah Department of Transportation (UDOT) traffic cameras to record over 10,000 hours of video, and manually counted almost 175,000 pedestrians crossing at the intersections. Using processed hourly pedestrian actuations and detections from ATSPM data, the authors estimated five non-linear regression models (segmented by pedestrian activity, cycle length, and pedestrian recall) using pedestrian signal data to predict hourly pedestrian crossing volumes. Overall, the estimates were strongly correlated with observed volumes (0.84) and had a low error (+/- 3.0 on average). These results—which use orders of magnitude more data than had previously been assembled on this topic—demonstrate the validity of using pedestrian data from traffic signals to estimate levels of pedestrian activity. The authors also developed a prototype dashboard to interactively visualize pedestrian signal data.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; Maps; Photos; References; Tables;
  • Pagination: 87p

Subject/Index Terms

Filing Info

  • Accession Number: 01759931
  • Record Type: Publication
  • Report/Paper Numbers: UT-20.17
  • Contract Numbers: 19-8237; 5H08462H
  • Created Date: Nov 24 2020 12:35PM