The effect of different aggregations of severity levels of crashes with pedestrians in urban areas

Abstract

Promoting a safer road environment for pedestrians requires an understanding of the risk factors associated with the injuries suffered by these users while involved in crashes. Injury levels as recorded by police reports may be subjected to bias and errors specially in adjacent and not extreme injury categories. The aim of this study is to investigate the impact of different severity classification configurations on identifying factors related to crashes involving pedestrians in urban areas. Multinomial logit models were estimated using crash records from the city of Fortaleza between the years 2017 and 2019. The results indicated that the combination of some severity levels can lead to different significant variables and, thus, depending on the specification of the response variable, the influence of important risk factors may end up being ignored in the model. Among the analyzed factors, the age of pedestrians, the day of the week, the time of the crash and the type of road remained significant for the different configurations of severity levels. In addition, the model with three severity categories (mild/moderate, severe, and fatal) presented the best performance in terms of model adjustment. It was observed from this model that factors such as the advanced age of pedestrians, crashes occurring at night, with heavy vehicles, on weekends and located on arterial or expressways are associated with more severe injuries.

Keywords

• Injury severity
• Pedestrian crash
• Multinomial logit model