Assessing Driving Risk Indicators from Large Driving Data Sets

Understanding the driving behaviours that lead to increased risk of collision for older drivers is a serious public safety challenge. Driving is important for older drivers to travel to activities that maintain an active independent lifestyle. Research has shown that varying levels of night driving, velocity and driving exposure may be risk factors. However, there is limited knowledge of what levels are indicative of higher risk for older drivers, especially as compared to a group of similar age peers. Drivers with behaviours significantly above or below the typical norm age group may indicate higher risk. For example, while lower levels of night driving could indicate lowered night-time crash risk due to self-regulation associated with reduced confidence or visual issues, greater amounts of nighttime driving may show increased risk related to over confidence or a need to drive at night. The paper analyzes a large dataset of naturalistic driving data that includes 1 year of driving data for 19,800 drivers and proposes normative models for night driving, driving speed and driving exposure for 5-year age groups from the youngest drivers (age 16) through to drivers over the age of 95. The models provide measures of the average and variance for drivers in each age group. This addresses a gap in the literature due to previous limitations of the available data or study population. The proposed measures allow for driver risk to be identified that is associated with atypical behaviour that is higher or lower than the age group norm. Models are proposed for night driving, velocity and driving exposure that allow the driving by other drivers to now be analyzed to identify with increased risk such as those that are outliers (+/- >2 SD from mean) in comparison to their age group peers.