Data Envelopment Analysis for the assessment of road safety in urban road networks: a comparative study using CCR and BCC models

Nearly 1.35 million people are killed annually on roads around the world and an additional 50 million are injured or disabled. Road traffic crashes are estimated to be the eighth leading cause of death globally for all age groups, the first among children and young people aged 5–29 years. These numbers highlight the urgency of the road safety issue for all governments and administrations. In their efforts to improve safety, road network managers can benefit from decision support tools able to assist them in monitoring and managing road safety interventions. This paper proposes a DEA-based decision support method to assist urban road safety management practitioners in identifying those roads where the needs to improve safety are the greatest. The method is applied to an Italian urban road network to define a hierarchy of hazardous road locations based on safety conditions. The social cost of accidents is used here for the first time as the only output indicator while the average number of conflict points at intersections and traffic flow are used as inputs. Both Constant Returns to Scale and Variable Returns to Scale DEA models, each oriented to input and output, are used. The comparison of the results makes it possible to identify the DEA model that seems best suited to be used as a decision support tool to advise urban road safety management by enabling a more careful definition of targeted priority lists of interventions.