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Abstract

Pedestrian traffic safety remains a persistent concern for transportation agencies in the United States, with pedestrian fatalities and serious injuries increasing substantially over the past decade. Cities in particular experience a significant proportion of traffic deaths due to the concentration of pedestrian traffic in these areas. Addressing this problem requires a better understanding of pedestrian exposure; however, transportation agencies often have limited resources for collecting pedestrian volume data, and pedestrian monitoring remains considerably less systematic than motor vehicle traffic monitoring. This research leverages pedestrian volume estimates derived from traffic signal push-button actuations, measures of walking, jogging, and running activity from Strava Metro, and characteristics of accessibility, land use, and transportation networks to develop predictive models for network-wide pedestrian volume estimation. The research demonstrates a pathway for resource-constrained transportation agencies to leverage existing and emerging data sources to expand pedestrian traffic monitoring and more systematically incorporate pedestrian exposure and systemic safety into state and local transportation agency practice.