Continuous crash risk estimation from discrete, multivariate crash count data

Abstract Crash risk affects every driver in some capacity and quantifying this risk is a key outcome in evaluating transportation safety. This study develops a statistical model for estimating continuous crash risk surfaces from discrete, multivariate crash count data. We focus on Interstate 15 (I-15) in Utah, USA, using aggregated crash counts released by the Utah Department of Transportation (UDOT). We model segment-level crash counts as a realization of an aggregated multivariate point pattern with continuous intensity surfaces; thus enabling continuous spatial risk estimation. We link roadway characteristics to crash risk using multivariate random forests, capturing nonlinear relationships and correlations between crash types. This approach supports the identification of high-risk areas and contributing roadway features, ultimately aiming to support decisions related to targeted safety interventions.