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. At-fault crash-prone drivers are usually considered as the high risk group for possible future incidents or crashes.In Louisiana, 34% of crashes areption 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. 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% wi th 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 plans meant to reduce crash proneness. The findings, it is recommended that crash-prone drivers should be targeted for special safety programs regularly through education and regulations.