Latest revision as of 23:16, 1 February 2021

Abstract

Considerable research has been carried out in recent years about crash modelling and to establish relationships between crash occurrences in different types of urban and rural roads and traffic flow parameters, geometric infrastructure characteristics, environmental factors and pavement qualifications. In this research crash-prediction models for 3 urban arterial highways in Tehran were set up on the basis of accident data observed during a 3-year monitoring period extending between 2006 and 2008. The candidate set of explanatory parameters were: traffic flow (pick hour volume), geometric infrastructure characteristics (section’s length, curvature, longitudinal slope, number of lanes) and pavement condition of surface wetness. Statistical analysis is done by SPSS on the basis of nonlinear regression modelling and during the analysis, principal components are identified to assist the principal component analysis method and more important variables recognized that could exhibit best description of crash occurrence. After studying variables for curves, it is shown that significant and efficient variables are section’s length (L), pick hour volume (PHV) and longitudinal slope (L.s), whereas for tangent they are section’s length (L), pick hour volume (PHV) and longitudinal slope (L.s) and curvature (1/R). Results indicate that the number of accidents increase with: L, PHV and L.s whereas they decrease with R. In addition, L.s is extremely effective in this research. The relation between speed variations with L.s and consequently traffic congestion, safety reduction and chance of accident occurrence are its efficiency causes. Furthermore, L.s affects driving site distance and driver behaviour such as passing other vehicles too. In addition, the effects of longitudinal surface friction and pavement are increased in plunge slope and they increase the chance of occurrence.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.2495/978-1-84564-847-3/011

• http://www.witpress.com/Secure/elibrary/papers/UT12/UT12049FU1.pdf

• http://www.witpress.com/Secure/elibrary/papers/UT12/UT12049FU1.pdf,

http://dx.doi.org/10.2495/ut120491

• https://www.witpress.com/Secure/elibrary/papers/UT12/UT12049FU1.pdf,

https://www.witpress.com/elibrary/wit-transactions-on-the-built-environment/128/23251,

https://academic.microsoft.com/#/detail/2052814889

DOIS: 10.2495/ut120491 10.2495/978-1-84564-847-3/011