• Journal of Industrial Convergence
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• v.17 no.4
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• pp.53-58
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• 2019

It is important to collect and analyze the data from the traffic accident analysis system and the National Statistical Office to reduce the traffic accident rate of the elderly, who are the weakest. In particular, it is more important to analyze the data in areas where the elderly population is large and where accidents occur frequently. This paper visualizes and analyzes the data of elderly traffic accidents that occurred in recent 5 years in the area where many elderly people live in Buyeo-gun. The elderly traffic accident type, accident area, and location data of the elderly can be useful for the improvement measures and related decision making to reduce the elderly traffic accidents.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.27-30
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• 2021

경제 성장과 함께 자동차의 수요가 늘어남에 따라 교통사고 발생 빈도는 꾸준히 증가하고 있다. 이에, 본 연구에서는 교통사고를 야기하는 도로 및 기상환경과 같은 조건을 활용하여 기계학습 모델을 통해 서울시 교통사고 사상자 수를 예측하는 모형을 찾고자 한다. 활용한 데이터는 도로교통 공단에서 제공하는 교통사고 사상자 수 정보를 포함하는 데이터로 2015년부터 2018년도까지 데이터를 학습에 사용하였고 2019년도 데이터를 테스트 평가에 사용하였다. 실증연구를 통해 트리 기반의 모델 별 성능을 비교하였으며 본 연구에 대한 결과는 사고 발생 시 우선순위에 의한 구조활동이 가능하게 함과 도로상황 및 기상을 고려한 안전운전 가이드 지식으로 활용될 수 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.90-102
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• 2022

Currently, in our society with a substantial and increasing fraction of the elderly population, transport safety for elderly drivers is becoming the center of attention. However, deficient data on vehicle crashes in South Korea limits the growth of traffic accident research pertaining to the country. So, we complemented South Korean vehicle crash data by examining USA vehicle crash data, especially the data of Ohio State, and analyzing the influential factors of elderly driver-involved crashes of the State. Subsequently, we suggested a way of improving the South Korean dataset. Notably, our study showed that the influential factors were vehicle speed, posted speed, and following other vehicles too close and provided them in the South Korean dataset.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.87-97
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• 2018

The traffic accident reports that are generated by the Traffic Broadcasting Networks(TBN) are unstructured data. It, however, has the value as some sort of real-time traffic information generated by the viewpoint of the drives and/or pedestrians that were on the roads, the time and spots, not the offender or the victim who caused the traffic accidents. However, the traffic accident reports, which are big data, were not applied to traffic accident analysis and traffic related research commonly. This study adopting text-mining technique was able to provide a clue for utilizing it for the impacts of traffic accidents. Seven years of traffic reports were grasped by this analysis. By analyzing the reports, it was possible to identify the road names, accident spot names, time, and to identify factors that have the greatest influence on other drivers due to traffic accidents. Authors plan to combine unstructured accident data with traffic reports for further study.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.393-399
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• 2005

본 논문은 현재 교통사고로 인한 피해가 심각한 상황에서 기존 교통사고 원인분석을 위한 많은 양의 자료를 효과적으로 처리할 수 있는 WebGIS 기반 교통사고 정보관리 시스템을 개발하였다. 더불어 교통정보를 어떻게 구성하고 어떤 방법으로 전달할 것인가에 대한 방안을 제시하고, 교통사고 정보에 대한 비공간적인 속성뿐만 아니라 도형자료와 기본적인 수치지도 등을 통합 관리할 수 있도록 하였다. 또한 교통사고 자료가 일반인들에게 제공되기 위한 준비 기간이 길었던 종래 방식에 비해 데이터 입력이 완료되는 즉시 인터넷을 통해 해당 지역의 교통사고 정보를 제공하는 실시간 교통사고 현황 정보제공 기능을 구현하였다. 이전의 단순한 집계에 의한 비공간적인 데이터를 사용한 교통사고 분석보다는 교통사고의 자세한 내용 및 통계자료와 함께 사고위치를 시각적으로 도시하여 가시적으로 주변 환경을 파악할 수 있는 기능을 제공하였으며 사고 담당 경찰관과 직접 연계할 수 있는 인터페이스를 제공하였다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.25-31
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• 2006

In this paper, a fatality model of car accident using data mining is proposed with the goal of reducing fatality of traffic accident. The analysis results with a proposed fatality model are utilized to improve a technology and environment for driving. For this, traffic accident data are collected, a data mining algorithm is applied to this data, and then, a fatality model of car accident is developed based on the analysis. The training data as well as test data are utilized to develop the fatality model. The important factors to cause fatality in traffic accidents can be investigated using the model. If these factors are taken into account in traffic policies and driving environment, it is expected that the fatality rate of traffic accident can be reduced hereafter.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.13-20
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• 2022

Road conditions and weather conditions are very important factors in the case of traffic accident fatalities in fog and ice sections that occur on roads in winter. In this paper, a simulation was performed to estimate the traffic accident risk rate assuming traffic accident prediction data. In addition, in this paper, in order to reduce traffic accidents and prevent traffic accidents, factor analysis and traffic accident fatality rates were predicted using the WEKA data mining technique and TENSOR FLOW open source data on traffic accident fatalities provided by the Korea Transportation Corporation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.168-181
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• 2023

This study analyzed the effects of traffic enforcement on accident reduction. The results revealed a significant reduction in both overall accidents (28.53%) and fatal accidents (39.44%). Notably, enforcement equipment targeting speed limits of 30 km/h and 50 km/h demonstrated similar accident reduction rates of 42.23% and 25.85%, respectively. However, variations were observed based on accident types and types of traffic violations. Therefore, it is evident that enforcement equipment yields distinct accident reduction effects depending on speed limits and types of traffic accidents. This finding underscores the potential for making informed policy decisions to enhance traffic safety measures.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.271-272
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• 2017

본 논문에서는 교통사고 데이터베이스 구축을 통해 교통사교 현황과 사고 당시의 여러 정황들을 파악할 수 있는 정보를 제공한다. 이 정보들에는 사고 당시의 기상상태, 도로형태, 차종, 연령, 성별 등의 데이터들이 포함되고 이러한 정보들을 바탕으로 데이터베이스 사용자들은 각 사고 별 종합적인 정보를 얻을 수 있다. 이를 통해 정부 당국 외에 보험사 등에 교통사고 관련 정책을 위한 유용한 정보들을 제공할 수 있다. 또한 운전자 개인들에게도 정보들을 제공해 교통사고를 보다 효율적으로 예방할 수 있다.