An analysis of the injury severity of pedestrians in Brazil using random parameters logit models

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Mateus Nogueira Silva (Author) Other; Flávio Cunto (Author) Other; Marcos Timbó Gomes (Author) Other; Sara Ferreira (Author) FEUP

Journal

Title: Transportes

Vol. 149

ISSN: 2237-1346

Publisher: Associação Nacional de Pesquisa e Ensino em Transportes

Indexing

Latindex

DOAJ

Google Scholar

Scientific classification

CORDIS: Technological sciences > Engineering > Civil engineering

FOS: Engineering and technology > Civil engineering

Other information

Authenticus ID: P-015-S1V

DOI: 10.14295/transportes.v31i1.2761

Abstract (EN): <jats:p>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.</jats:p>

Language: English

Type (Professor's evaluation): Scientific