Comparison of contributing factors in hit-and-run crashes with distracted and non-distracted drivers

Among different types of crashes, hit-and-run is driver's failure to stop after a vehicle crash. There are many accidents where drivers could actually be at fault or totally innocent, and leaving the scene would turn an innocent driver into a criminal. The current paper aims to contribute to the literature by exploring the association of different variables pertaining to the condition of infrastructure, environment, driver, population of the area, and crash severity and type with hit-and-run crashes. The analysis is performed for two data sets: (i) crashes where the driver was distracted; and (ii) crashes where driver was not distracted. Hit-and-run crash data with corresponding factors are police-reported data for crashes within Cook County, Illinois, occurring between 2004 and 2012. A logistic regression model assessed 43 variables within 16 categories for statistically significant association with hit-and-run crashes, for drivers with and without distraction. For both driver distraction statuses, 17 variables were associated with a significant increased probability of a hit-and-run crash and 10 variables were associated with a significant decreased probability. Additionally, it was found that crashes on curve level and curve hillcrest road alignment types were associated with increased likelihood of a hit-and-run crash when the driver was distracted and decreased likelihood when the driver was not distracted. Variables related to hit-and-run crashes vary depending on driver's distraction status. When comparing likelihood to flee the scene after a crash, non-distracted drivers are 27% less likely to do so compared to distracted drivers. (C) 2015 Elsevier Ltd. All rights reserved.