Random Parameter Models for Accident Prediction on Two-Lane Undivided Highways in India

Generalized linear modeling (GLM) approach with the assumption of Poisson or negative binomially distributed error has been employed by many researchers in modeling road accidents. A number of explanatory variables related to traffic, geometry and environment, that contribute to accident occurrence, have been identified and models have been proposed. The accident prediction models reported in literature largely employ fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representation traffic composition. But the mixed traffic on Indian highways comes with lot of variabilities within, ranging from difference in vehicle types to variability in driver behavior. Random parameter models, that allow for heterogeneity in the model parameters is expected to be appropriate for the Indian situation. The present study is an attempt to employ random parameter modelling for accident prediction on two-lane undivided rural highways in India. Three years accident history from nearly 200 km of highway segments is used to calibrate and validate the models. The results of the analysis suggest that the model parameters for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, driveway density and horizontal and vertical curvatures are randomly distributed. The paper is concluded with a discussion on estimation results and the limitations of the present study.

Language

  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 10p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01153363
  • Record Type: Publication
  • Report/Paper Numbers: 10-1884
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 10:52AM