Objectives: Changes in the physical and mental abilities of elderly road users have led to an important question of how to define elderly. In this article, both common and diverse contributory factors to elderly pedestrian injuries are investigated, by segmenting the elderly into the younger-old (65-74) and older-old (75+).
Methods: An 8-year collision data set in Seoul, South Korea, was utilized, where injury levels were defined as severe vs. nonsevere. Three binary logit models-single contributory factor; age and single factor; and age and joint factors-were modeled using 17 predictor variables to evaluate odds ratios with middle-aged (14-64) pedestrians as a reference group.
Results: In the single contributory factor model, we found that older age was the most critical risk factor leading to severe injury. In the interaction model of age and single contributory factor, higher odds ratios were observed in the older-old than the younger-old for all predictor variables. A set of common contributory factors for both elderly groups was identified, including near overpass crossing, roadside, drunk, and truck. On the other hand, uphill, downhill, nighttime, and sidewalk were found to be a much higher risk to the older-olds. The age and joint factor analysis revealed amplifying effects among risk factors when considered in combination, especially among older-old pedestrians.
Conclusions: The study investigated the commonality and diversity of pedestrian injuries among the elderly by introducing an additional cutoff age of 75. By employing single and interaction binary logit models, the study identified common risk factors for both elderly groups, as well as those that are particularly hazardous to the older-old. With nearly every country experiencing growth in the elderly population, our study strongly suggests that the conventional definition of a single elderly group is no longer relevant and the variety among elderly pedestrians needs to be considered in traffic safety policy.
Keywords: Elderly pedestrian; aging society; binary logit model; interaction model; risk factor.