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http://dx.doi.org/10.14400/JDC.2021.19.4.025

Classifying Severity of Senior Driver Accidents In Capital Regions Based on Machine Learning Algorithms  

Kim, Seunghoon (City and Regional Planning, The Ohio State University)
Lym, Youngbin (Center for Innovation Strategy and Policy, KAIST)
Kim, Ki-Jung (Department of Smart Car Engineering, Doowon Technical University)
Publication Information
Journal of Digital Convergence / v.19, no.4, 2021 , pp. 25-31 More about this Journal
Abstract
Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.
Keywords
Traffic accident analysis; Senior driver; Crash severity; Machine learning; Classification;
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Times Cited By KSCI : 1  (Citation Analysis)
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