• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.167-173
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• 2009

It is important to study on the traffic accident severity reduction because traffic accident is an issue that is directly related to human life. Therefore, this research developed countermeasure to reduce traffic accident severity considering various factors that affect the accident severity. This research developed the Accident Severity Prediction Model using the collected accident data from Seohaean Expressway in 2004~2006. Through this model, we can find the influence factors and methodology to reduce accident severity. The results show that speed limit violation, vehicle defects, vehicle to vehicle accident, vehicle to person accident, traffic volume, curve radius CV(Coefficient of variation) and vertical slope CV were selected to compose the accident severity model. These are certain causes of the severe accident. The accidents by these certain causes present specific sections of Seohaean Expressway. The results indicate that we can prevent severe accidents by providing selected traffic information and facilities to drivers at specific sections of the Expressway.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.1-14
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• 2020

The aim of this study is to analyze effects of factors on the number of vehicles involved in traffic accidents on freeway sections. In previous studies about traffic accident severity, the analysis of accidents involving multiple vehicles was insufficient. However, multiple-vehicle accidents are likely to cause casualties and are the main reasons increasing accident duration and social costs. In this study, the number of vehicles involved in an accident was interpreted as the result of the accident, not as the cause of the accident, and the impacts of each accident factor were analyzed using a multinomial logit model. The results indicate that multiple-vehicle accidents are mainly related to following factors: nighttime, driver's faults, obstacles on the road, a downhill slope, heavy vehicles, and freeway mainline sections including tunnels and bridges.

• Journal of Korean Society of Transportation
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• v.34 no.6
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• pp.489-498
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• 2016

The objective of this study is to develop road traffic accident model involving child pedestrian especially at school zones and its surrounding area. The analysis is based upon traffic accident data collected near sixty elementary schools in City of Cheongju during 2012 and 2014. This study results in two statistical models ; one is to predict the number of road traffic accidents involving children, and the other is to predict EPDO(Equivalent Prperty Damage Only). These models are represented as Poisson models. which are statistically significant with the likelihood ratios of 0.533 and 0.273. The common explanatory variables of these models are the ratio of road section with more than 4 lanes, the number of entrance and exit, the number of signalized crosswalk in school zone, the number of school zone signage including road surface marking, and the number of speed limit signs. The specific variables are the length of road stretch in school zone, the number of reflector mirrors, and the number of signalized crosswalk outside school zone. It is concluded that these types of road safety facilities can reduce the number of traffic accidents involving children at school zones and its surrounding area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.29-40
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• 1999

복잡한 현대 문명 생활을 영위하는 가운데 각종 위험물질이 날마다 생산되어 운반되어지고 있다. 이러한 위험물질이 운반되는 과정에서 위험물사고발생은 불가피하다. 위험물사고는 발생확률은 낮지만 일단 사고발생시 인명, 환경, 재산피해가 심각하기 때문에 가능한 한 피해를 최소화하기 위한 대책이 필요하다. 본 연구에서는 공로수송에 있어 위험물질운송에 따른 경로별 위험도 분석을 위한 모형을 개발하고, 본 모형에 기초하여 계산된 링크별 위험도를 낮추기 위한 대책을 제시하였다. 각 링크별 위험도를 계산하기 위해서는 링크별 교통량, 사고 데이타 및 위험물질별 사고시 피해영향규모 등을 결정해야 하는 바, 이러한 작업은 사실상 수작업으로 불가능하다. 이를 자동적으로 수행하는 방안으로 지리정보시스템을 활용하였다 또한, 여러 가지 위험물질별 위험도 분석에서 위험물사고 저감대책, 위험물사고시 피해최소화 대책 등 일련의 작업을 하나의 시스템에서 이루어지도록 하는 의사결정지원시스템 형태의 위험물수송관리시스템으로 개발하였다. 최종적으로는 위험물질 중 대부분을 차지하는 석유.화학물질을 가장 많이 다루는 지역인 울산지역을 시범지역으로 선정하여 본 연구에서 제시한 모형을 적용해 보았다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1017-1023
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• 2017

Duration induced by freeway crashes has a critical influence on traffic congestion. In general, crash duration composes detection and verification, response, and clearance time. Of these, the crash clearance time determined by a crash clearance team has attracted considerable attention in the freeway congestion management since the interest of the first two time stages faded away with increasing ubiquitous mobile phone users. The objective of this study is to identify the critical factors that affect freeway crash clearance time using a Cox's proportional hazard model. In total, 6,870 crash duration data collected from 30 major Korean freeways in 2013 were used. As a result, it was found that crashes during the night, with trailer or larger size truck, and in tunnel section contribute to increasing clearance time. Crashes associated with fatality, completed damage of crashed vehicle (s), and vehicles' fire or rollover after crash also lead to increasing clearance time. Additionally, an increase in the number of vehicles involved resulted in longer clearance time. On the other hand, crashes in the vicinity of tollgate, by passenger car, during spring, on flat section, and of car-facility type had longer clearance time. On the basis of the results, this paper suggested some strategic plans and mitigation measures to reduce crash clearance time on Korean freeway systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.46-54
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• 2009

This study deals with the effects on the accident reduction according to the installation of RLC (red light cameras). The objective is to analyze the effects on the accident reduction using EB (Empirical Bayes) method. In pursuing the above, the study uses the 728 accident data occurred at the 28 intersections which RLC are installed. The main results are as follows. First, the effects of accident reduction were analyzed to be 20.74% by simple before-after study method. Second, the safety performance functions (SPF) were developed by the Poisson and negative binominal regression models, and since the over-dispersion parameter was close to zero, Poisson model was evaluated to be more appropriate than the negative binominal model. Also, the Poisson model was analyzed to be statistically significant because its ${\rho}^2$ value was 0.409. Finally, the results of analysis using an EB method showed that the accidents were reduced by range from 3.89 to 29.23%.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.139-150
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• 2008

In this study, It is focused on development of the forecasting model about trumpet InterChange(IC) ramp accident because of the frequency of accident in ramp more than highway basic section and trend the increasing accident in ramp. The independent variables was selected through statistical analysis(correlation analysis, multi-collinearity etc) by ramp types(direct, semi-direct and loop). The independent variables and accident rate is non-linear relationship. So it made new variables by transformation of the independent variables. The forecasting models according to exit-ramp type (direct, semi-direct and loop) are built with statistical multi-variable regression using all possible regression method. And the forecasts of the models showed high accuracy statistically. It is expected that the developed models could be employed to design trumpet IC ramp more cost-efficiently and safely and to analyze the causes of traffic accidents happened on the IC ramp.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.1-15
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• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.112-112
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• 2020

하천에서 유해화학물질 유입 사고 발생 시 수환경 피해를 최소화하기 위해 신속한 초기 대응이 필요하다. 따라서, 본 연구에서는 수환경 화학사고 대응 시스템 구축을 위해 하천 실시간 모니터링 지점에서 관측된 유해화학물질의 농도 자료를 이용하여 발생원의 유입 지점과 유입량을 역추적하는 프레임워크를 개발하였다. 본 연구에서 제시하는 프레임워크는 첫 번째로 하천 저장대 모형(Transient Storage Zone Model; TSM)과 HEC-RAS 모형을 이용하여 다양한 유량의 수리 조건에서 화학사고 시나리오를 생성하는 단계, 두번째로 생성된 시나리오의 유입 지점과 유입량에 대한 시간-농도 곡선 (BreakThrough Curve; BTC)을 21개의 곡선특징 (BTC feature)으로 추출하는 단계, 최종적으로 재귀적 특징 선택법(Recursive Feature Elimination; RFE)을 이용하여 의사결정나무 모형, 랜덤포레스트 모형, Xgboost 모형, 선형 서포트 벡터 머신, 커널 서포트 벡터 머신 그리고 Ridge 모형에 대한 모형별 주요 특징을 학습하고 성능을 비교하여 각각 유입 위치와 유입 질량 예측에 대한 최적 모형 및 특징 조합을 제시하는 단계로 구축하였다. 또한, 현장 적용성 제고를 위해 시간-농도 곡선을 2가지 경우 (Whole BTC와 Fractured BTC)로 가정하여 기계학습 모형을 학습시켜 모의결과를 비교하였다. 제시된 프레임워크의 검증을 위해서 낙동강 지류인 감천에 적용하여 모형을 구축하고 시나리오 자료 기반 검증과 Rhodamine WT를 이용한 추적자 실험자료를 이용한 검증을 수행하였다. 기계학습 모형들의 비교 검증 결과, 각 모형은 가중항 기반과 불순도 감소량 기반 특징 중요도 산출 방식에 따라 주요 특징이 상이하게 산출되었으며, 전체 시간-농도 곡선 (WBTC)과 부분 시간-농도 곡선 (FBTC)별 최적 모형도 다르게 산출되었다. 유입 위치 정확도 및 유입 질량 예측에 대한 R²는 대부분의 모형이 90% 이상의 우수한 결과를 나타냈다.