Highlights Curves, particularly those that are horizontal, concentrate 54% of fatal accidents on rural highways in Brazil. A Generalized Estimating Equation (GEE) model was successful for its application in Brazil. Brazil lacks exclusive model the frequency and severity of crashes. Satellite images may be the way to overcome the problem of the lack of project documentation for road safety in Brazil. The GIS can develop a database capable of associating accident records with the geometric parameters of the highway.

    Abstract Due to the need to update the current guidelines for highway design to focus on safety, this study sought to build an accident prediction model using a Geographic Information System (GIS) for single-lane rural highways, with a minimum of statistically significant variables, adequate to the Brazilian reality, and improve accident prediction for places with similar characteristics. This analysis was conducted on 215 km of single-lane road segments of highway BR-232 in the State of Pernambuco. The development of a database made it possible to associate accident records for the period 2007 to 2016 from Federal Highway Police (PRF) data with the geometric parameters of the highway, obtained through geometric reconstruction of the vector data available at the National Department of Transportation Infrastructure (DNIT) and the semi-automatic extraction of highways from satellite imagery. The homogeneous segments were analyzed and classified by the Spatial method (Kernel-KDE density). A Generalized Estimating Equation (GEE) model was estimated to model the frequency and severity of accidents. The results indicate that increase in the slope and the radius impact the increase in the frequency of accidents and the reduction of the severity of accidents in curves.


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    Title :

    Traffic accident prediction model for rural highways in Pernambuco



    Publication date :

    2021-12-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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