A New Methodology for Accidents Analysis: The Case of the State Road 36 in Italy

A New Methodology for Accidents Analysis: The Case of the State Road 36 in Italy

Fabio Borghetti Giovanna Marchionni Matteo De Bianchi Benedetto Barabino Michela Bonera Claudia Caballini

Mobility and Transport Laboratory – Design Department – Politecnico di Milano, Italy

Department of Civil Engineering, Architecture, Land, Environment and Mathematics (DICATAM) – University of Brescia, Italy

Department of Environment, Land and Infrastructure Engineering (DIATI) – Politecnico di Torino, Italy

Available online: 
| Citation



Every year more than 1.35 million people die for road accidents and several million suffer serious injuries, which force them to live with compromised health conditions. Over the last decades, road safety research has focused on improving modelling techniques. However, due to the lack of expertise and statistical skills, such approaches might not be used by local authorities and road managers for road safety evaluation purposes. This paper proposes an operational methodology to analyze road accidents with the aim of increasing road safety. More specifically, the methodology enables to identify the most critical road segments to prioritize economic resources allocation accordingly. by using the data collected by the Road Police Department of Lombardy Region (in Italy) from 2014 to 2018, this methodology has been successfully applied to State Road 36, which is recognized as one of the busiest roads in Italy with a very high number of accidents occurring every year. The proposed methodology may support public administrations and road managers – involved in the definition and implementation of safety measures – to reduce the number of road accidents identifying and implementing prioritized interventions. Moreover, the methodology is general enough to be applied to each segment of a generic road infrastructure.


decision support system, GIS, road accident analysis, road safety analysis, road safety framework, road transportation infrastructure, statistical data


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