Along with age-related factors, geographical settings—urban, suburban, and rural areas—also contribute to the differences in fatal crashes among older drivers. These differences in crash outcomes might be attributed to the various driving challenges faced by older drivers residing in different locations. To understand these challenges from the perspective of the older driver, a focus group study was conducted with drivers 65 and older from urban, suburban, and rural settings. Guided-group interviews were used to assess driving challenges, mobility options, opportunities for driver support systems (DSS), and alternate transportation needs. Content analysis of the interview responses resulted in four categories representing common challenges faced by older drivers across the settings: behavior of other drivers on the road, placement of road signs, reduced visibility of road signs due to age-related decline, and difficulties using in-vehicle technologies. Six categories involved location-specific challenges such as heavy traffic situations for urban and suburban drivers, and multi-destination trips for rural drivers. Countermeasures implemented by older drivers to address these challenges primarily involved route selection and avoidance. Technological advances of DSS systems provide a unique opportunity to support the information needs for route selection and avoidance preferences of drivers. Using the content analysis results, a framework was built to determine additional and modified DSS features to meet the specific challenges of older drivers in urban, suburban, and rural settings. These findings suggest that there is heterogeneity in the driving challenges and preferences of older drivers based on their location. Consequently, DSS technologies and vehicle automation need to be tailored to not only meet the driving safety and mobility needs of older drivers as a population, but also to their driving environment.

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Conventional approaches to modelling driver risk have incorporated measures such as driver gender, age, place of residence, vehicle model, and annual miles driven. However, in the last decade, research has shown that assessing a driver’s crash risk based on these variables does not go far enough—especially as advanced technology changes today’s vehicles, as well as the role and behavior of the driver. There is growing recognition that actual driver usage patterns and driving behavior, when it can be properly captured in modelling risk, offers higher accuracy and more individually tailored projections. However, several challenges make this difficult. These challenges include accessing the right types of data, dealing with high-dimensional data, and identifying the underlying structure of the variance in driving behavior. There is also the challenge of how to identify key variables for detecting and predicting risk, and how to combine them in predictive algorithms. This paper proposes a systematic feature extraction and selection framework for building Comprehensive Driver Profiles that serves as a foundation for driver behavior analysis and building whole driver profiles. Features are extracted from raw data using statistical feature extraction techniques, and a hybrid feature selection algorithm is used to select the best driver profile feature set based on outcomes of interest such as crash risk. It can give rise to individualized detection and prediction of risk, and can also be used to identify types of drivers who exhibit similar patterns of driving and vehicle/technology usage. The developed framework is applied to a naturalistic driving dataset—NEST, derived from the larger SHRP2 naturalistic driving study to illustrate the types of information about driver behavior that can be harnessed—as well as some of the important applications that can be derived from it.