Existing methods of pedestrian travel monitoring are generally inefficient for collecting pedestrian data in many locations over long time periods. In this study, we demonstrate the validity of using a novel and relatively ubiquitous big data source—pedestrian data from high-resolution traffic signal controller logs—as a way of estimating pedestrian crossing volumes. Every time a person presses a pedestrian push button or a pedestrian call is registered at a signal, this information can be logged and archived. To validate these pedestrian signal data against observed pedestrian counts, we recorded over 10,000 h of video at 90 signalized intersections in Utah, and counted around 175,000 people walking. For each hour and crossing, we compared these observed counts to measures of pedestrian activity calculated from traffic signal data, using a set of five simple piecewise linear and quadratic regression models. Overall, our results show that traffic signal data can be successfully used to estimate pedestrian crossing volumes with good accuracy: model-predicted volumes were strongly correlated (0.84) with observed volumes and had a low mean absolute error (3.0). We also demonstrate how our models can be used to estimate annual average daily pedestrian volumes at signalized intersections and identify high pedestrian volume locations. Transportation agencies can use pedestrian signal data to help improve pedestrian travel monitoring, multimodal transportation planning, traffic safety analyses, and health impact assessments.

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A deeper understanding of how weather variables affect pedestrian volumes is important, as active travelers are an essential part of a sustainable transportation system. Pedestrian data are limited for investigating the impacts of weather on walking levels, with most studies having data at only a couple of locations. Pedestrian actuation data (from push-buttons at traffic signals) overcomes this limitation. The Utah Department of Transportation archives pedestrian push-button press data for use in its Automated Traffic Signal Performance Measures system. In this study, pedestrian actuation data was used as a proxy for walking activity and weather data was collected from the National Oceanic and Atmosphere Administration. Using 15 months of daily time series data in Cache County, the impacts of weather on pedestrian signal activity were examined at 49 signalized intersections, using a log-linear time series regression analysis with categorical step-wise weather variables. The findings revealed that snow depth had the most frequent negative effect on walking activity. Snowfall (> 0.6 in.) also tended to have negative impacts when significant. Very hot maximum temperatures (≥ 90°F) were associated with lower pedestrian activity at around one-third of signals. Very low minimum temperatures (< 20°F) were also associated with lower pedestrian activity. Precipitation had a negative effect on walking levels, but at only a few signals. The study’s key findings offer implications for multimodal transportation planning (winter maintenance, shade trees, etc.) and traffic signal operations.

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This work investigated the impacts of COVID-19 on pedestrian behavior, answering two research questions using pedestrian push-button data from Utah traffic signals: How did push-button utilization change during the early pandemic, owing to concerns over disease spread through high-touch surfaces? How did the accuracy of pedestrian volume estimation models (developed pre-COVID based on push-button traffic signal data) change during the early pandemic? To answer these questions, we first recorded videos, counted pedestrians, and collected push-button data from traffic signal controllers at 11 intersections in Utah in 2019 and 2020. We then compared changes in push-button presses per pedestrian (to measure utilization), as well as model prediction errors (to measure accuracy), between the two years. Our first hypothesis of decreased push-button utilization was partially supported. The changes in utilization at most (seven) signals were not statistically significant; yet, the aggregate results (using 10 of 11 signals) saw a decrease from 2.1 to 1.5 presses per person. Our second hypothesis of no degradation of model accuracy was supported. There was no statistically significant change in accuracy when aggregating across nine signals, and the models were actually more accurate in 2020 for the other two signals. Overall, we concluded that COVID-19 did not significantly deter people from using push-buttons at most signals in Utah, and that the pedestrian volume estimation methods developed in 2019 probably do not need to be recalibrated to work for COVID conditions. This information may be useful for public health actions, signal operations, and pedestrian planning.