Abstract
The study aims to develop a support procedure to estimate the efficacy of infrastructural interventions to improve road safety. The study was carried out on a 110 km stretch of the A3 highway, in southern Italy. Data from a huge sample concerning traffic, geometry and accidents for two periods of the same duration were compared, for which cluster analysis, and in particular, the “hard c means” binary partition algorithm was employed. Using cluster analysis, all the accidents with strong similarities were aggregated. Then for each cluster, the “cluster representative” accident was identified, to find the average among the various characteristics (geometrical, environmental, accident-related). A “hazard index” was also created for each cluster, whereby it was possible to establish the danger level for each “cluster”. Using this information, an accident prediction model using a multi-variate analysis was produced. This model was used as a support for decision-making on infrastructures and to simulate situations to which the Before-After technique could be applied.
Highlights
Introduction and Previous StudiesAll policies that affect travel patterns affect the numbers killed and injured in transport accidents, and changing the travel patterns may in itself be a way of reducing these numbers
This study has addressed a problem of “Before-After” analysis
The multivariate model (11) was derived from the average characteristics of the accidents contained in each grouping and expressed in terms of a hazard index (10)
Summary
All policies that affect travel patterns affect the numbers killed and injured in transport accidents, and changing the travel patterns may in itself be a way of reducing these numbers. A recent study (Ghosh & Savolainen, 2012) examined the factors that affect the time required by the Michigan Department of Transportation Freeway Courtesy Patrol to clear incidents occurring on the southeastern Michigan freeway network. These models were developed using traffic flow data, roadway geometry information, and an extensive incident inventory database. Dell’Acqua et al (2011a) applied cluster analysis to develop a Decision Support System (DSS) useful to indentify the Accident Modification Factors (AMF)
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