Recognising risk factors associated with crash frequency on rural four lane highways

Abstract

Engineers and researchers in the transportation discipline have tried to build safe roads following appropriate design standards, but traffic crashes are unavoidable. Crash pattern could be detected if accurate prediction models capable to predict various traffic crashes are developed. To obtain the maximum possible crash reduction effects with limited budgetary resources, it is important that measures are based on engineering and scientific surveys to study cause of crashes. The research presented here aims to evaluate Road Safety using Bayesian methods on a section of four-lane National Highway (NH)-58 located in the state of Uttarakhand, India. This paper compares Poisson Regression, Poisson Log-Normal and Poisson Gamma modelling techniques using a full Bayes hierarchical approach for Indian highway crash data. Explanatory variables for highway segment analysis were Geometric Characteristics like Curvature Change Rate, Transverse Slope and Traffic Characteristics as Average Daily Traffic, Volume to Capacity ratio were analyzed against dependent variable as Crash Count per two hundred meter per year. © 2016 Taylor & Francis Group, London.