Crash prediction models for older drivers: A panel data analysis approach

The graying of America is resulting in a larger proportion of older individuals\nin the population. Recent transportation surveys show that an increasing number\nof older individuals are licensed to drive and that they drive more than their\nsame age cohort a decade ago. These trends necessitate increased study of their\npotential highway safety problems. Considerable progress has been made on\nunderstanding older drivers safety issues. Nonetheless, research has been\nrather limited and the findings inconclusive. One of the methodological\nlimitations is the lack of considering temporal order between events (i.e., the\ntime between onset of medical condition, symptom, and crash). Without\ntime-series data, researchers have often linked a "snap-shot" of medical\nconditions and driving patterns to more than one year of crash data, hoping to\naccumulate enough data on crashes. The interpretation of the results from these\nstudies is difficult in that one cannot explicitly attribute the increase in\nhighway crash rates to medical conditions and/or physical limitations. This\npaper uses a panel data analysis approach to identify factors that place older\ndrivers at greater crash risk. Our results show that factors that place female\ndrivers at greater crash risk are different from those influencing male drivers.\nMore risk factors were found to be significant in affecting older mens\ninvolvement in crashes than older women. When the analysis controlled for the\namount of driving, women who live alone or who experience back pain were found\nto have a higher crash risk. Similarly, men who are employed, score low on\nword-recall tests, have a history of glaucoma, or use antidepressant drugs were\nfound to have a higher crash risk. The most influential risk factors in men\nwere the amount of miles driven, and use of antidepressants.\n