TITLE:
Road Crash Prediction Models: Different Statistical Modeling Approaches
AUTHORS:
Azad Abdulhafedh
KEYWORDS:
Crash Prediction Models, Poisson, Negative Binomial, Zero-Inflated, Logit and Probit, Neural Networks
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.7 No.2,
April
30,
2017
ABSTRACT: Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Crash frequency refers to the prediction of the number of crashes that would occur on a specific road segment or intersection in a time period, while crash severity models generally explore the relationship between crash severity injury and the contributing factors such as driver behavior, vehicle characteristics, roadway geometry, and road-environment conditions. Effective interventions to reduce crash toll include design of safer infrastructure and incorporation of road safety features into land-use and transportation planning; improvement of vehicle safety features; improvement of post-crash care for victims of road crashes; and improvement of driver behavior, such as setting and enforcing laws relating to key risk factors, and raising public awareness. Despite the great efforts that transportation agencies put into preventive measures, the annual number of traffic crashes has not yet significantly decreased. For in-stance, 35,092 traffic fatalities were recorded in the US in 2015, an increase of 7.2% as compared to the previous year. With such a trend, this paper presents an overview of road crash prediction models used by transportation agencies and researchers to gain a better understanding of the techniques used in predicting road accidents and the risk factors that contribute to crash occurrence.