Crash Prediction Models for Older Drivers: A Panel Data Analysis Approach
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Crash Prediction Models for Older Drivers: A Panel Data Analysis Approach

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English
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  • Subject/TRT Terms:
  • Resource Type:
    Tech Report
  • TRIS Online Accession Number:
    784570
  • NTL Classification:
    NTL-SAFETY AND SECURITY-Highway Safety;NTL-SAFETY AND SECURITY-Human Factors;
  • Abstract:
    The graying of America is resulting in a larger proportion of older individuals

    in the population. Recent transportation surveys show that an increasing number

    of older individuals are licensed to drive and that they drive more than their

    same age cohort a decade ago. These trends necessitate increased study of their

    potential highway safety problems. Considerable progress has been made on

    understanding older drivers safety issues. Nonetheless, research has been

    rather limited and the findings inconclusive. One of the methodological

    limitations is the lack of considering temporal order between events (i.e., the

    time between onset of medical condition, symptom, and crash). Without

    time-series data, researchers have often linked a "snap-shot" of medical

    conditions and driving patterns to more than one year of crash data, hoping to

    accumulate enough data on crashes. The interpretation of the results from these

    studies is difficult in that one cannot explicitly attribute the increase in

    highway crash rates to medical conditions and/or physical limitations. This

    paper uses a panel data analysis approach to identify factors that place older

    drivers at greater crash risk. Our results show that factors that place female

    drivers at greater crash risk are different from those influencing male drivers.

    More risk factors were found to be significant in affecting older mens

    involvement in crashes than older women. When the analysis controlled for the

    amount of driving, women who live alone or who experience back pain were found

    to have a higher crash risk. Similarly, men who are employed, score low on

    word-recall tests, have a history of glaucoma, or use antidepressant drugs were

    found to have a higher crash risk. The most influential risk factors in men

    were the amount of miles driven, and use of antidepressants.

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