Jorge Tiago Bastos

Bootstrapping DEA Scores for Road Safety Strategic Analysis in Brazil

AbstractIn this paper, three risk indicators on road safety are combined into a composite indicator in order to assess the overall fatality risk for the 27 Brazilian states using the so-called multiple layer data envelopment analysis model. The states are first clustered and next, a range of bootstrapped scores is generated to manifest the estimated variability in the road safety performance. Bootstrapping the original DEA scores showed to be a useful strategy to assess the robustness of the states’ ranking.

BOOTSTRAPPING DEA SCORES FOR TRAFFIC FATALITY RISK ASSESSMENT IN BRAZIL

In this paper, three risk indicators on road safety (i.e., traffic fatalities per vehicle, per traveled kilometer and per inhabitants) are combined into a composite indicator in order to assess the overall fatality risk for the 27 Brazilian states. The so-called multiple layer data envelopment analysis (DEA) model is used in this respect. Given their remarkable diversity in terms of road safety context, the states are first clustered (into four clusters) and next, a range of bootstrapped scores is generated to manifest the estimated variability in the road safety performance of each state. Bootstrapping the original DEA scores showed to be a useful strategy to assess the robustness of the states’ ranking, which is subjected to uncertainties in input information and DEA model determinism.

Assessing Road Safety Performance by Data Envelopment Analysis - The Case of Brazil

The intense economic growth experienced by Brazil in recent decades and its consequent explosive motorization process have generated an undesirable impact: the increasing and unbroken trend in road traffic fatalities. In order to contribute to road safety diagnosis, this study presents a research on two main available indicators in Brazil: mortality rate (represented by fatalities per inhabitants); and fatality rate (represented by two sub-indicators, i.e., fatalities per vehicle and fatalities per vehicle kilometers traveled). These indicators were aggregated into a composite indicator (CI) through a multiple layer data envelopment analysis CI model (ML DEA-CI), in which the optimum combination of indicators’ weights is searched for each decision-making unit (DMU), in this case, the 27 Brazilian states (BR-27). The composite indicator represents the road safety performance on which a ranking of states can be made. As a result, valuable differences associated to DMU’s socioeconomic attributes were outlined after calibrating the model.