Factors related to highway crash severity in Brazil through a multinomial logistic regression model

Abstract

Reducing the number of deaths by road crashes is an important priority for many countries around the world. Although focusing on the occurrence of crashes can provide safety policies that help reduce its numbers, studying their severity can provide different measures that may help further reduce the number of deaths by focusing on the most severe problems first. In this paper, a mul.nomial logis.c regression model is fi:ed to na.onwide highway crash data in Brazil from 2017 to 2019 to iden.fy and es.mate the associated factors to crash severity. Severity is classified as without injury, with injured vic ms or with fatal vic ms. Amongst other observa.ons, results indicate that pedestrian involvement in highway crashes increase drama.cally their severity. Also, condi.ons that favor greater speeds (clear weather, .mes when there are fewer vehicles on the road) are also related to an increase in crash severity, poin.ng to a propor.onal rela.on with traffic fluidity. Moreover, some observed limita.ons on the data may indicate that Brazil would benefit greatly from na.onal crash records standards and unified databases, especially crossmatching crash reports with health data.