Literature DB >> 23376544

An approach to accidents modeling based on compounds road environments.

Ana Fernandes1, Jose Neves.   

Abstract

The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2013        PMID: 23376544     DOI: 10.1016/j.aap.2012.12.041

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  2 in total

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Authors:  A Alizadeh; M Zare; M Darparesh; S Mohseni; M Soleimani-Ahmadi
Journal:  J Med Life       Date:  2015

2.  A knowledge elicitation approach to traffic accident analysis in open data: comparing periods before and after the Covid-19 outbreak.

Authors:  ChienHsing Wu; Shu-Chen Kao; Chia-Chen Chang
Journal:  Heliyon       Date:  2022-08-24
  2 in total

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