Class
Article
College
College of Engineering
Presentation Type
Oral Presentation
Abstract
Existing data collection methods for pedestrian travel monitoring are generally difficult, expensive, and/or time-consuming. In this study, we demonstrate the validity of using a novel and relatively ubiquitous big data source, pedestrian push button data from traffic signals'contained within one state's (Utah's) Automated Traffic Signal Performance Measure (ATSPM) system'as a proxy measure of pedestrian activity. Every time a person pushes a push button or makes a pedestrian call to cross the street, this information can be logged and archived in a central database. We used observed pedestrian counts (from recorded videos) to validate these signal-based pedestrian activity measures. We recorded multiple days of video at nearly 90 randomly selected signalized intersections throughout Utah, and then manually counted pedestrian events. Although data collection continues, we have already watched over 5,000 hours of footage and counted well over 30,000 pedestrians. For each hour, we then compared our pedestrian volumes to pedestrian signal data from ATSPM. Our preliminary results show generally good correspondence between pedestrian signal events and pedestrian volumes (71% of signals had correlations greater than 0.75), demonstrating the relative accuracy of such proxy signal data for estimating pedestrian activity levels. Transportation agencies can use pedestrian signal data to help improve pedestrian travel monitoring, multimodal transportation planning, traffic safety analyses, and heath impact assessments.
Start Date
4-8-2020 12:00 PM
End Date
4-8-2020 1:00 PM
Validating Pedestrian Traffic Signal Push Button Data as a Measure of Walking Activity at Signalized Intersections
Existing data collection methods for pedestrian travel monitoring are generally difficult, expensive, and/or time-consuming. In this study, we demonstrate the validity of using a novel and relatively ubiquitous big data source, pedestrian push button data from traffic signals'contained within one state's (Utah's) Automated Traffic Signal Performance Measure (ATSPM) system'as a proxy measure of pedestrian activity. Every time a person pushes a push button or makes a pedestrian call to cross the street, this information can be logged and archived in a central database. We used observed pedestrian counts (from recorded videos) to validate these signal-based pedestrian activity measures. We recorded multiple days of video at nearly 90 randomly selected signalized intersections throughout Utah, and then manually counted pedestrian events. Although data collection continues, we have already watched over 5,000 hours of footage and counted well over 30,000 pedestrians. For each hour, we then compared our pedestrian volumes to pedestrian signal data from ATSPM. Our preliminary results show generally good correspondence between pedestrian signal events and pedestrian volumes (71% of signals had correlations greater than 0.75), demonstrating the relative accuracy of such proxy signal data for estimating pedestrian activity levels. Transportation agencies can use pedestrian signal data to help improve pedestrian travel monitoring, multimodal transportation planning, traffic safety analyses, and heath impact assessments.