• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

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.

• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.13-19
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• 2018

The purpose of this study was to evaluate the effect of air bag deployment in passenger car head-on collisions on injuries to the driver. The drivers in head-on collisions who were brought to the emergency rooms of two hospitals from January 2011 and October 2014 were evaluated, as were the vehicles involved. The driver injury level were assessed by utilizing Collision Deformation Classification (CDC) codes, and the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS), respectively. In this study, it was shown that the chest ISS and AIS were significantly high when an air bag only is deployed. A statistically significant difference was found in the crush extent when the driver who fastened the seatbelt was found to be affected more than the ISS 9. Even when an air bag is deployed in a head-on car collision, injury severity can vary according to accident circumstances and crash severity. Accordingly, first aid can be rapidly given, and the injured person can be quickly referred to a hospital, only if the assessment of persons involved in a vehicle accident is accurately carried out.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.40-46
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• 2020

Modern traffic accidents are a complex occurrence. Various indicators are needed to analyze traffic accidents. Countries that have been investigating traffic accidents for a long time accumulate various data to analyze traffic accidents. The Korean In-Depth Accident Study (KIDAS) database collected damaged vehicles and severity of injury caused by Collision Deformation Classification code (CDC code), Abbreviated Injury Scale (AIS), and Injury Severity Score (ISS). As a result of the investigation, data relating to the injuries of the occupants can be easily obtained, but it was difficult to analyze human severity based on the information of the damaged vehicle. This study suggests a method to measure the speed change at the time of an accident, which is one of the most important indicators in the vehicle crash database, to help advance KIDAS research.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.20-26
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• 2018

The fatality of rollover accidents in motor vehicle crashes is high despite their low incidence. Through the investigation of a 12-passenger van rollover accident in which 10 passengers were involved, we intend to analyze the correlation between the severity of the injury and the position of the occupants. We collected accident information from medical records, interviews, photo-images of the damaged van, field surveys, and the results of the Korean New Car Assessment Program (KNCAP). Based on the occupants' position, we classified injury sites and estimated injury severity. Passenger injury severity was evaluated by trauma score calculation. The initiation type of the rollover accident was passenger side 'fall-over' and the Collision Deformation Classification (CDC) code for the damaged van was 00TDZO3. The crash of the van involved 10 passengers, with an average age of $16.3{\pm}4.2years$. Few of the occupants had fastened seat belts at the time of the incident, and there was no airbag installed. One patient sustained severe liver injury and another was diagnosed with a fracture of the right humerus. The most common injuries were at the upper extremities and the neck. The average of Injury Severity Score (ISS) was $4.8{\pm}5.9$, and the average ISS of right-seated, mid-seated and left-seated occupants was $7.5{\pm}9.3$, $1.5{\pm}0.7$, and $3.3{\pm}2.1$ respectively (p>0.05). In the rollover (to-passenger side) accident of occupant unfastened, the average ISS of right-seated occupants (near side) was higher, but there was no statistically significant difference.