TY  - JOUR
PY  - 2015//
TI  - Estimating likelihood of future crashes for crash-prone drivers
JO  - Journal of traffic and transportation engineering (English edition)
A1  - Das, Subasish
A1  - Sun, Xiaoduan
A1  - Wang, Fan
A1  - Leboeuf, Charles
SP  - 145
EP  - 157
VL  - 2
IS  - 3
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2095-7564
UR  - http://dx.doi.org/10.1016/j.jtte.2015.03.003
ID  - ref1
ER  -