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Journal Article

Citation

Colagrande S. Transp. Res. Proc. 2022; 60: 504-511.

Copyright

(Copyright © 2022, Elsevier Publications)

DOI

10.1016/j.trpro.2021.12.065

PMID

unavailable

Abstract

As is known, road accidents essentially depend on four interrelated factors: human behaviour, vehicle efficiency, environmental conditions and the characteristics of the infrastructure. Although the vast majority of accidents is attributable to the first three factors, almost always attributable to improper user behaviour, it is of fundamental importance to try to reduce that part of the risk attributable to the infrastructure. In this research, the problem concerning the assessment of urban road safety in existing roads, in order to identify the dangerous sections on which to concentrate resources to make the functional adjustments deemed necessary, is addressed through the analysis of the accident rate found in operation. In particular, the research proposes a new model that characterizes the intrinsic risk of a urban road infrastructure through the assessment of accidents, disaggregated into different types, with a risk index that is a function of the two most significant variables that represent the accidents: the frequency with which they occur and the severity of the damages produced. The study also provides three aspects for achieving improved urban road safety. The first identifies the critical road sections (blackspots), through the application of the new model to the classic methods of accident assessment. The second defines the functional adjustments necessary to reduce the causes of accidents by comparing the risk of accidents, determined with the new methodology for each type of accident, and the technical characteristics of the road network in question. The third establishes the intervention priorities, based on an economic planning linked to the available budget, among the functional adjustments identified to reduce the risk of accident.


Language: en

Keywords

Road accident analysis; road accident frequency; road accident severity

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