Abstract/Details

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


2008 2008

Other formats: Order a copy

Abstract (summary)

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.

Indexing (details)


Subject
Adult education;
Industrial engineering;
Transportation planning;
Older people;
Roads & highways
Classification
0516: Adult education
0546: Industrial engineering
0709: Transportation planning
Identifier / keyword
Social sciences, Education, Applied sciences, Active learning, Evaluation, Older drivers, Road scanning, Scanning, Simulation, Situation-based learning, Training
Title
Improving the road scanning behavior of older drivers through the use of situation -based learning strategies
Author
Romoser, Matthew Ryan Elam
Number of pages
295
Publication year
2008
Degree date
2008
School code
0118
Source
DAI-B 69/12, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549915980
Advisor
Fisher, Donald L.
Committee member
Fisher, Donald L.; Krishnamurty, Sundar; Woolf, Beverly P.
University/institution
University of Massachusetts Amherst
Department
Industrial Engineering & Operations Research
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3337011
ProQuest document ID
304577116
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304577116
Access the complete full text

You can get the full text of this document if it is part of your institution's ProQuest subscription.

Try one of the following:

  • Connect to ProQuest through your library network and search for the document from there.
  • Request the document from your library.
  • Go to the ProQuest login page and enter a ProQuest or My Research username / password.