Estimating likelihood of future crashes for crash-prone drivers

Article English OPEN
Das, Subasish ; Sun, Xiaoduan ; Wang, Fan ; Leboeuf, Charles (2015)
  • Publisher: Elsevier BV
  • Journal: Journal of Traffic and Transportation Engineering (English Edition), volume 2, issue 3, pages 145-157 (issn: 2095-7564)
  • Related identifiers: doi: 10.1016/j.jtte.2015.03.003
  • Subject: Roadway safety | Crash risk | Crash-prone drivers | TA1001-1280 | Sensitivity | Logistic regression | Transportation engineering

At-fault crash-prone drivers are usually considered as the high risk group for possible future incidents or crashes. In Louisiana, 34% of crashes are repeatedly committed by the at-fault crash-prone drivers who represent only 5% of the total licensed drivers in the state. This research has conducted an exploratory data analysis based on the driver faultiness and proneness. The objective of this study is to develop a crash prediction model to estimate the likelihood of future crashes for the at-fault drivers. The logistic regression method is used by employing eight years' traffic crash data (2004–2011) in Louisiana. Crash predictors such as the driver's crash involvement, crash and road characteristics, human factors, collision type, and environmental factors are considered in the model. The at-fault and not-at-fault status of the crashes are used as the response variable. The developed model has identified a few important variables, and is used to correctly classify at-fault crashes up to 62.40% with a specificity of 77.25%. This model can identify as many as 62.40% of the crash incidence of at-fault drivers in the upcoming year. Traffic agencies can use the model for monitoring the performance of an at-fault crash-prone drivers and making roadway improvements meant to reduce crash proneness. From the findings, it is recommended that crash-prone drivers should be targeted for special safety programs regularly through education and regulations.
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