County-level crash prediction models for Pennsylvania accounting for income characteristics

Abstract

The majority of current safety prediction models utilize roadway and traffic data as independent variables to describe safety performance at a microscopic level. Recent work moves toward predicting these measures within some region as a function of roadway and traffic data, as well as non-traditional variables, such as socioeconomic measures. This paper aims to provide a holistic view of the intersection of socioeconomics and safety in Pennsylvania by investigating possible relationships between wealth and various aspects of safety performance, including crash frequency, severity, and cost. The analyses presented in this paper serve as case studies with intentions to promote the development of more robust, wealth-inclusive safety analyses in the future. The study reveals relationships between socioeconomic-related measures and crash frequency, severity, and cost estimations. These relationships indicate counties with increased levels of socioeconomic distress (quantified by multiple socioeconomic-related variables) are estimated to experience more crashes – particularlyrelated to alcohol usage – and higher total crash costs, and crashes that occur in counties with increased levels of socioeconomic distress are estimated to be more likely to result in an increased injury severity level compared to crashes that occur elsewhere. These resultssupport previous work andexpand on that work by considering multiple socioeconomic-related variables and their impacts on three unique safety-related measures. The existence of a relationship between crash frequency, severity, and cost and wealth-related variables opens the door to further exploration of including wealth in traditional safety analyses. This paper discusses these relationships and offers recommendations for future work.