• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.477-484
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• 2023

The number and age of elderly drivers are continuously increasing according to the extension of the human lifespan. Therefore, in transportation, efforts are being made to differentiate and manage elderly drivers by age group. This study aims to identify the factors affecting the crash severity of early and late elderly drivers, compared to middle-aged drivers, and to identify the characteristics between these groups. Crash data that occurred on nationwide roads for the past 5 years (2017-2021) was applied. Unlike previous studies, this study only targeted drivers in their 40s and older, when presbyopia begins: middle-aged driver (40-64), early elderly driver (65-74), and late elderly driver (75+). As a result of logistic regression analysis, a total of 18 variables were found to affect serious injuries including fatalities in early and late elderly drivers. Most of these variables appeared to lead to severity more sensitively in the late elderly group. The results of this study are expected to be useful as basic information for establishing traffic safety policies for elderly drivers in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.134-145
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• 2024

The purpose of this study was to propose a framework for developing test scenarios for issuance of conditional driver's licenses. The framework was composed of five stages. Initially, we reviewed the literature on traffic crash characteristics in terms of accident frequency and severity regarding the main factors of crashes caused by older drivers. In the second stage, the characteristics of crashes attributed to non-elderly, early elderly, and late elderly drivers were analyzed using data obtained from the Traffic Accident Analysis System (TAAS), and crash types for elderly drivers were derived. In the third stage, black box videos of high-risk crash types were analyzed to derive crash stories that described the circumstances in which crashes occurred. In the fourth step, crash situations were classified by rating the types of crash stories derived to develop various scenarios. Step 5 involved creating a scenario by applying the PEGASUS 5-Layer format, which has recently been used to develop test scenarios for autonomous vehicles. The results of this study are expected to be used as a basis for developing driving ability evaluation scenarios for the issuance of conditional driver's licenses.