• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.597-600
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• 2000

계속적인 증가 추세를 보이고 있는 교통량으로 인해 환경 문제뿐 아니라 교통사고로 인한 사상자 및 물적피해가 상당량으로 집계되고 있다. 본 논문에서는 데이터융합 및 앙상블 클러스터링방법을 이용한 교통사고 심각도 분류분석방법을 제안함으로서 교통사고예방에 기여하고자 한다. 이를 위하여 신경망과 Decision-Tree기법을 이용하여 얻은 물적피해와 신체상해가 발생할 확률을 융합하는 전형적인 데이터 융합기법(템스터-쉐퍼, 베이지안 방법, 로지스틱융합방법)을 사용하였다. 또한, 분류정확도를 향상시키고자 Bootstrap 재추출 방법을 이용해 얻어진 여러 개의 분류예측 결과 중 다수의 분류결과를 선택하는 앙상블 (arcing, bagging)기법을 적용하였다. 더불어, 본 연구에서는 클러스터링 방법을 제시하고, 이 방법이 기존의 융합기법, 앙상블기법과 비교한 결과, 분류예측면에서 정확도가 향상됨을 보였다.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.601-607
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• 2010

비신호교차로 사고를 대상으로 신호기 운영방법과 차로폭에 따른 고령운전자 교통사고 특성을 파악하기 강화군, 무주군, 원주시, 보령시의 3년간(2007년-2009년) 교통사고 데이터를 사용하여 일반운전자와 고령운전자의 사고를 비교, 분석하였다. 그 결과, 고령운전자 교차로 교통사고는 점멸신호기로 운영되는 교차로사고는 일반운전자보다 다소 높았으며, 정면충돌사고 일반운전자 사고의 8배나가 높게 나타났다. 또한 고령운전자 교차로 교통사고는 차로폭이 6m초과 13m이하의 교차로에서 41.0%가 발생하여 왕복 3-4차로에서의 교차로에서 고령운전자의 교통사고 발생률이 높은 것으로 나타났다. 따라서 고령운전자의 교차로 교통사고 예방을 위해서는 6m초과 13m이하의 도로에 대한 대책이 마련되어야 할 것이며, 점멸신호기로 운영되는 교차로에 대한 점검이 필요하다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.117-118
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• 2014

최근 공공기관의 공공데이터 제공이 활성화 되고 있으며, 이를 활용한 응용서비스에 대한 요구도 증가하고 있는 추세이다. 현재 교통정보예측 플랫폼은 실시간 교통정보 또는 과거 교통정보이력을 분석하여 미래의 교통량이나 도착시간정보를 제공하고 있으나 날씨, 사고 등과 같은 미래 교통정보에 즉각적인 영향을 줄 수 있는 요소를 배제하고 있어 높은 신뢰도를 확보하기 어렵다. 본 논문에서는 교통정보예측에 영향을 주는 요소인 기상, 사고, 교통정보와 같은 공공데이터를 효율적으로 수집 저장 처리할 수 있는 저장방식 및 신뢰도 높은 교통정보를 예측할 수 있는 예측기술이 포함된 b-Traffic 서비스 플랫폼을 제시한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.89-96
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• 2016

According to the promoting policy for bicycle riding, the bicycle road infrastructure in Korea has been widely established. As the number of bicycle rider increases, bicycle traffic accidents also increase year after year. In this paper, we analyze bicycle traffic accident data from 2007 to 2014 which is provided by Road Traffic Authority and present statistical results of bicycle traffic accidents. And also regression analysis is applied to predict the number of daily traffic accidents in Seoul using ASOS(Automated Synoptic Observing System) climate data observed in the Seoul sector which are provided by Korea Meteorological Administration. In addition, decision tree analysis techniques are used to forecast the level of traffic accidents severity. In the analytic results of this research, we expect that it will be helpful to establish the collective policy of bicycle accident data and protective strategy in order to reduce the number of bicycle accidents.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.360-362
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• 2012

교통수단의 발달과 생활수준의 향상으로 도로에 차량이 많이 늘어나고 교통사고가 많이 발생함에 따라, 교통사고 자동인식 시스템에 관한 연구가 많이 진행되고 있다. 본 논문에서는 카메라의 위치에 따라 두 객체의 관심영역 사이의 겹침을 해석하는 것이 달라져 규칙이 변하는 것을 방지하고, 사람의 추론과정과 같이 교통사고를 퍼지 규칙으로 모델링하여 획득한 데이터가 부정확할 경우에 발생하는 잘못된 추론을 보정하기 위한 퍼지 규칙기반 시스템을 제안한다. 카이스트 삼거리에서 촬영한 9개의 사고 시나리오 데이터에 대해 실험하여 DR 87.34%, CDR 89.13%, FAR 10.75%의 결과를 얻었고, 이를 기존의 규칙기반 시스템, 규칙-확률 시스템과 비교하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.490-492
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• 2021

In order to realize a safety society in traffic accidents, Korea prepared comprehensive government-wide measures in 2017. Efforts are being made to minimize accidents while walking by children and the elderly by lowering the speed limit in urban areas from 60 km to 50 km and limiting the vehicle to 30 km in the case of child protection zones. In this study, after pre-processing each data with the status of vehicle registration and traffic accident spatial data (GIS) by designating a specific area, Danyang-gun, where the rate of child traffic accidents is increasing every year, it is intended to understand the structure of the data and find out the structural pattern of the data analytical studies were conducted.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.427-441
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• 2021

Purpose: This study proposes a method for developing a model that predicts the probability of traffic accidents in advance to prevent the most frequent traffic accidents in the military. Method: For this purpose, CRISP-DM (Cross Industry Standard Process for Data Mining) was applied in this study. The CRISP-DM process consists of 6 stages, and each stage is not unidirectional like the Waterfall Model, but improves the level of completeness through feedback between stages. Results: As a result of modeling the same data set as the previously constructed accident investigation data for the entire group, when the classification criterion was 0.5, Significant results were derived from the accuracy, specificity, sensitivity, and AUC of the model for predicting traffic accidents. Conclusion: In the process of designing the prediction model, it was confirmed that it was difficult to obtain a meaningful prediction value due to the lack of data. The methodology for designing a predictive model using the data set was proposed by reorganizing and expanding a data set capable of rational inference to solve the data shortage.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.35-51
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• 1998

This article aims at presenting a new framework for traffic accident analysis by proposing a new methodology for the management of the accident data and by establishing the relationship between accidents and roadway characteristics within it For the first issue, authors introduced geographic information system (GIS) into the analysis framework of the accident data since it is believed that analysis based on GIS seems to provide more effective information in reducing accidents. Point-based, line-based, and polygon (grid)-based approaches were set of along with concrete examples. Especially, the location-based scores such as localization, specialization coefficients, and Tress score have been added to identify the intensity of certain accident types within study area or grids. The second issue addressed the equation formulation of accident and fatality numbers with roadway characteristics like number of intersections and road length in a grid with a sense that (1) accidents on roadways are the function of the roadway physical characteristics rather than the socio-economical secondary data (2) the equation can be applied to the any 'suggested' area, not just region or nation, and (3) the accident forecasting model should emphasize the accident location itself more than any other factors. Some equations based on those assumption have been derived along with some future research agenda.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.160-173
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• 2021

Recently, research on the development of autonomous vehicles has increased worldwide. Moreover, a means to identify and analyze the characteristics of traffic accidents of autonomous vehicles is needed. Accordingly, traffic accident data of autonomous vehicles are being collected in California, USA. This research examined the characteristics of traffic accidents of autonomous vehicles. Primarily, traffic accident data for autonomous vehicles were analyzed, and the text data used text-embedding techniques to derive major keywords and four topics. The methodology of this study is expected to be used in the analysis of traffic accidents in autonomous vehicles.