Crash Estimation Models for Intersections

Abstract

The focus of this paper is to develop models to estimate the number of crashes at intersections. Data collected at 150 intersections in the City of Charlotte, North Carolina were used in the development of models. Factors such as demographic (population and households), socio-economic (income and employment), network (number of lanes, speed limit, signalized or not, skewed, number of left-turn and right-turn lanes and traffic volume by turning movement) and land use (commercial and residential) characteristics at each intersection were considered as independent variables. The numbers of crashes at each intersection was used as a dependent variable. Generalized linear models were developed to estimate risk at intersections. The role of spatial proximity in capturing data to develop models was also examined. These models provide valuable insights on characteristics that contribute to crashes at intersection. They could be used by practitioners to estimate potential risk at new intersections and at intersections near new developments so as to proactively apply appropriate treatments.