Development of a Prediction Model for Crash Occurrence by Analyzing Traffic Crash and Citation Data

Using citation and accident data can help predict traffic events

Highway Safety has been identified as the top priority in US and all around the world. Traffic crashes have been identified as the fourth cause of deaths in the world according to the Center for Disease Control and Prevention (2013) behind hearth diseased, cancer and respiratory problems. In fact, the effects of traffic crashes cost billions of dollars per year in the US alone.

It is commonly acknowledged that several risk factors such as human behavior and vehicle characteristics may highly contribute to the occurrence of traffic crashes. In addition, research has been conducted in the attempt to understand the relationship between traffic violations and traffic crash records. Some of these studies have found that there is a positive correlation between the number of traffic violations and the likelihood of traffic crash involvement. Meanwhile, other studies have found that with an increase in traffic violations, a decrease in traffic crashes was observed.

This study proposed the analysis of traffic violation and traffic crash records in order to develop a probabilistic model that will help detect high risk drivers with the main goal of preventing future crashes. This research approach includes the study of the existing traffic violation records and traffic crash record databases, identify possible variables that can be selected for the development of the model, and at the same time identify new variables that can be incorporated resulting in the improvement of the probabilistic model and easily detect future high risk drivers. This research approach includes filtering the database of traffic violations and traffic crashes, evaluation of the fitted model using several statistical tests and goodness of fit methods, and selecting the model that represents a better fit for the phenomena under study.