Assessing the effectiveness of road safety measures in Barcelona (2013-2018).

Introduction: This article aims to determine the effectiveness and extent of measures taken to decrease the severity of traffic crashes in Barcelona from 2013 to 2018. This will be achieved through an analysis of the traffic crash data.

Method: Our approach involves the use of binary logistic regression models.

A discrete mixture regression for modeling the duration of non-hospitalization medical leave of motor accident victims.

Studies analyzing the temporary repercussions of motor vehicle accidents are scarcer than those analyzing permanent injuries or mortality. A regression model to evaluate the risk factors affecting the duration of temporary disability after injury in such an accident is constructed using a motor insurance dataset. The length of non-hospitalization medical leave, measured in days, following a motor accident is used here as a measure of the severity of temporary disability.

Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries.

The analysis of factors influencing the severity of the personal injuries suffered by victims of motor accidents is an issue of major interest. Yet, most of the extant literature has tended to address this question by focusing on either the severity of temporary disability or the severity of permanent injury. In this paper, a bivariate copula-based regression model for temporary disability and permanent injury severities is introduced for the joint analysis of the relationship with the set of factors that might influence both categories of injury.