Risk Factors Analysis for Drivers with Multiple Crashes

Abstract

Identifying the drivers who frequently involvement in traffic crashes (ie high-risk drivers) is the main concern of all road safety related entities. The factors that belong to the driver’s behavior are considered as leading causes of traffic crashes. Drivers’ behavior can be measure based on their historical records of traffic rules violations and crashes involvements. This paper aims to investigate the characteristics of drivers who are frequently involved in severe crashes and to define the parameters that can be used to recognize the risky drivers. Historical records of about 324,644 drivers during eight years from 2008 to 2015 were analyzed. About 20 types of unsafe traffic violation types are investigated in details. The interrelationships between the at-fault drivers involved in traffic crashes during the study period and their demographic characteristics, historical records of their severe and property damage only (PDO) crashes and historical violations of total types and specific types of violations were explored. Negative Binomial Regression modeling approach is applied to define the associated variables that can be used to predict the driver’s severe crashes involvements. The results show that females, young, local, and less driving experience drivers have higher risk to be involved in future severe crashes. In addition, the following violations can be used as predictors to define drivers with multiple crashes: exceeding speed limit by more than 50 kph, car racing involvement, alcohol use, mobile use, tailgating, entering road suddenly, not using helmet, and overtaking-related violations. The findings can also be used to develop or improve the preventative