Advances in pedestrian travel monitoring: Temporal patterns and spatial characteristics using pedestrian push-button data from Utah traffic signals

Abstract

In this study, we advanced pedestrian travel monitoring using a novel data source: pedestrian push-button presses obtained from archived traffic signal controller logs at more than 1,500 signalized intersections in Utah over one year. The purposes of this study were to: (1) quantify pedestrian activity patterns; (2) create factor groups and expansion/adjustment factors from these temporal patterns; and (3) explore relationships between patterns and spatial characteristics. Using empirical clustering, we classified signals into five groups, based on normalized hourly/weekly counts (each hour’s proportion of weekly totals, or the inverse of the expansion factors), and three clusters with similar monthly adjustment factors. We also used multinomial logit models to identify spatial characteristics (land use, built environment, socio-economic characteristics, and climatic regions) associated with different temporal patterns. For example, we found that signals near schools were much more likely to have bimodal daily peak hours and lower pedestrian activity during out-of-school months. Despite these good results, our hourly/weekday patterns differed less than in past research, highlighting the limits of existing infrastructure for capturing all kinds of activity patterns. Nevertheless, we demonstrated that signals with push-button data are a useful supplement to existing permanent counters within a broader pedestrian traffic monitoring program.