Improving the road scanning behavior of older drivers through the use of situation -based learning strategies

• Main Author: Romoser, Matthew Ryan Elam
• Language: ENG
• Published: ScholarWorks@UMass Amherst 2008
• Subjects: Adult education|Industrial engineering|Transportation planning
• Online Access: https://scholarworks.umass.edu/dissertations/AAI3337011

Older drivers are over-represented in angled crashes when compared with younger experienced drivers. Past research primarily points to age-related cognitive and physical decline, which can impede older drivers' ability to monitor their driving environment efficiently and decrease their ability to maintain adequate situational awareness. Despite compensatory behaviors such as driving less, driving more slowly or avoiding driving in inclement conditions, there is evidence that in some cases these drivers may be under-compensating, as older drivers are still involved in more angled crashes than any other category. Of particular concern are intersections in which other vehicles can approach from the side. Two experiments described here investigate whether tailored feedback based on a driver's own unsafe behaviors and active, situation-based training in a simulator can change drivers' attitudes about their own abilities, raise their awareness of the crash risks for older drivers and lead to long-term improvements of driving behavior such as increased side-to-side scanning while negotiating intersections. Experiment 1 investigated whether customized feedback tailored to the individual's specific unsafe driving behaviors in a simulator can successfully alter an older driver's perceptions of his driving skills. Experiment 2 compared how effectively customized feedback about a driver's specific unsafe driving behaviors on the open road followed by active situation-based training in a simulator can improve road scanning and head turning behavior when compared with lecture-style training. The results from Experiment 1 demonstrated that letting drivers make errors in a simulator and then providing customized feedback was successful in changing older drivers' perception of their ability, making them more willing to change driving behavior. The results from Experiment 2 indicated that capturing drivers' errors on the road, providing customized feedback, and then adding active training in a simulator increased side-to-side scanning in intersections by nearly 100% in both post-training simulator and field drives. A second group, which received passive classroom-style training, demonstrated no significant improvement. In summary, compared with passive training programs, error capture, feedback, and active situation-based practice in a simulated environment is a much more effective strategy for raising awareness and increasing the road scanning behavior of older drivers.