Understanding Crash and Non-driving Related Task Engagement Patterns of At-risk Drivers: A Comparison of Non-driving Related Task Clusters and an Analysis of Naturalistic Driving

Abstract

There are nearly 35,000 fatal automobile crashes in the US each year. The two most vulnerable road user groups, based on fatality rates per mile driven, are younger drivers, age 16-24, and older drivers, age 65 and up. Understanding the similarities and differences in driving behaviors between these two groups can inform crash mitigation strategies. For this dissertation, which included 3 studies, the Second Strategic Highway Research Project (SHRP2) Naturalistic Driving Study (NDS) was utilized to analyze naturalistic driving behavior to better understand crash patterns and non-driving task engagement patterns for these age groups, as well as for middle-age drivers. The SHRP2 NDS classified over 40 specific non-driving related (NDR) tasks, many of which differ in how they distract drivers. To better understand the patterns of engagement for these NDR tasks these tasks were categorized into larger groups or clusters. How those groupings impacted driver distraction, crash severity, and crash likelihood were then analyzed. In Study One, two approaches to categorizing NDR tasks based on Multiple Resource Theory (MRT;Wickens 1980) were compared. The first divided tasks based on perceptual modality and processing code (visual tasks only). The second approach divided tasks solely via perceptual modality. A significant main effect on distraction was found for age under both scenarios, with small effect sizes. However, no significant effect was found for either of the MRT classification methods on distraction. The same interaction between age and MRT task types was also not significant. Study Two involved categorizing NDR tasks using a data-driven method of classifying NDR tasks based on naturalistic driving data. The relationships between age and these new classifications, were found to be significant. Study Three compared the NDR task grouping methods from Study One and Study Two. Two logistic regressions were run analyzing the effect of age, maneuver judgement, gender, and NDR task engagement, on crash likelihood. The methods of NDR task grouping from the previous studies were used for the regressions. Residuals for both were compared to find the best model for grouping types of NDR tasks, which was found to be the MRT model, but with minimal model fit. Overall these findings show that by investigating new classifications of NDR tasks, previously unseen relationships between NDR tasks, age, and other crash risk factors can be found which may help to reduce the crash rates of at risk driving populations like teen and older drivers.