Random parameter models for accident prediction on two-lane undivided highways in India

IntroductionGeneralized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the 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 representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.MethodThe present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.ResultsThe results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.ConclusionsThe paper is concluded with a discussion on modeling results and the limitations of the present study.

Research highlights
► The present study employs random parameters modeling approach to model accident frequencies on Indian two-lane undivided rural highways, which is a novel attempt. The facts that there is large variability in safety performance of Indian highways across locations and that random parameter modeling can capture that variability to a great extent compared to conventional fixed parameter modeling make the present study relevant.
► The results of the study show that the model coefficients of many variables, related to traffic and road geometry and environment, are in fact random. The results are encouraging and suggest random parameter modeling approach as a potential candidate for development of accident prediction models for Indian highways.
► The models developed in the present study could be used for safety evaluation of Indian two-lane undivided rural highways. These models are expected to give a more realistic picture as they capture some of the variability in safety levels of highways, which is otherwise unaccounted for in conventional fixed parameter models.