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.

In Brazil, pedestrians represent the third largest group of crash victims, after motorcyclists and car occupants. Implementing measures to ensure pedestrian safety and prioritization requires an understanding of the risk factors associated with crash injuries. In this study, a random-parameter logit model was estimated to investigate factors influencing the severity of crashes with pedestrians in urban roads in Fortaleza, Brazil. A sample of 2,660 observations of crashes with pedestrians in the city from 2017 to 2019 was used. The injury severity levels adopted by the Crash Information System (SIAT) were grouped into three categories: mild/moderate, severe and fatal. From the investigated factors, only the variable related to the pedestrian's age over 60 years old obtained a significant random parameter. In this case, the heterogeneity in the observations may be associated, among other factors, to the body’s physical fragility and the cognitive function that may differ among individuals in this group. The results showed that the driver’s gender and age, the crash site, the motorcycle use, and the presence of speed cameras did not have a significant impact on the severity of crashes with pedestrians. On the other hand, crashes occurring at night, with heavy vehicles, on weekends, and located on roads with higher traffic classification are associated with more severe injuries. The incorporation of unobserved heterogeneity in the estimation of the model's parameters stands out as one of the main contributions of this work.