Crash Prediction Models for Intersections on Rural Multilane Highways

Abstract

Accident prediction models are often used to estimate the number of accidents on segments and at intersections in the road network. Most often the models are developed for a total number of crashes for the facility or for crashes by severity. However, the frequency and severity of crashes of different types can be expected to vary according to underlying phenomena that cause them. To account for this variation better, modeling of accidents at intersections on rural four-lane highways in California is described separately for four different collision types: opposite-direction crashes, same-direction crashes, intersecting-direction crashes, and single-vehicle crashes. The findings from this modeling are reported with a special focus on the differences in crash types by (a) severity distribution, (b) dependence on traffic flow, and (c) variables that best explain between-site variations in the occurrence of different crash types. Evident differences exist in severity as well as the relationship of flow between several of the crash types. Intersecting and opposite-direction crashes are more severe than same-direction crashes. Same- and opposite-direction crashes exhibit similar relationships with traffic flow, but there are differences compared with intersecting-direction crashes and single-vehicle crashes. In addition, the variables that turn out to be good predictor variables differ somewhat for each crash type.