Big Data Methodologies for Simplifying Traffic Safety Analyses

Project Description Due to the low rate of crashes/near-crash events, most drivers and majority of trips are event-free. This high imbalance is challenging to researchers as traditional statistical methods and data mining tools are not successful at dealing with rare event data. Until now, there has been little research done on this matter, making SHRP2 NDS data a great resource for this study. SHRP2 NDS data as it is not only the largest NDS to date, this collection contains 3,400 drivers, 35 million miles of continuous driving as well as 1,500 crashes and thousands of near-crashes. This study is focused on identifying kinematic variables and optimal threshold values that predict high-risk drivers using rare event modeling as well as a variety of other statistical and data mining models. It has been shown that driver's higher elevated gravitational-force rates are highly correlated with their crash/near-crash rate, making it a favorable characteristic to asses risk on a driver level. Analysis will be conducted on a trip level to eliminate environmental confounding variables such as; driving time, average speed, road conditions, etc.. Data Request Scope The scope of the data requested is primarily kinematic information of high g-force, trip summary, event, driver demographic, and basic vehicle information. Data Specifications Driver Demographics Questionnaire Sleep Habits Questionnaire Driving Knowledge Survey Clock Drawing Score Barkley's ADHD Screening Test Sensation Seeking Scale Survey Driver Behavior Questionnaire Event Details Table Trip Summary Table Vehicle Details Table High G-force Event Table