• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.170-178
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• 2014

Until now, several research on the relationship of traffic crash occurrences and geometric had been conducted and revealed that projects of road alignment, geometric improvement and hazardous segment selection reduced the number of accidents and accident severities. However, such variables did not consider the non-homogeneous characteristics of roadway segments due to the difficulty of data collection, which results in under-estimation of the standard error affecting the overall modeling goodness-of-fit. This study highlights the importance of non-homogeneity by looking at the effect of the non-homogeneous geometric variables through the modeling process. The model delivers meaningful results when using some geometric variables without relevant geometrics' variables.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.14-25
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• 2018

In recent years, it has become technically easier to explain factors related with traffic accidents in the Big Data era. Therefore, it is necessary to apply the latest analysis techniques to analyze the traffic accident data and to seek for new findings. The purpose of this study is to compare the predictive performance of the negative binomial regression model and the deep learning method developed in this study to predict the frequency of traffic accidents in expressways. As a result, the MOEs of the deep learning model are somewhat superior to those of the negative binomial regression model in terms of prediction performance. However, using a deep learning model could increase the predictive reliability. However, it is easy to add other independent variables when using deep learning, and it can be expected to increase the predictive reliability even if the model structure is changed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.91-99
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• 2015

This study intends to build a traffic accident predictive model considering road geometrics, traffic and enviromental characteristics and identify the relationship of 4-legs intersection accidents in Seoul and Busan metropolitan area. The RPNB(Random Parameter Negative Binomial) model shows improvement over the fixed NB(Negative Binomial) and out of 53 variables, 10 variables (main road number of lane, main road vehicle traffic volume(left), minor road vehicle traffic volume(right), main road drive restriction, minor road sight distance, minor road median strip, minor road speed limit, minor road speed restriction) showed to have significant variables affecting traffic accident occurrences in 4-legs signilized intersections. Also, among 10 significant variables, 2 variables(minor road sight distance, minor road speed restriction) found to be random parameters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1279-1287
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• 2014

The purpose of this study is to develop the U-turn accident model at signalized intersections in urban areas. The characteristics of the accidents which are associated with U-turn operation at 3 and 4-legged signalized intersections was analyzed and the U-turn accident model was developed by regression analysis in Changwon city. First, in order to analyze the effectiveness on traffic accidents by U-turn installation, the difference of mean of traffic accident number are measured between two groups which are composed by whether or not U-turn installation the groups by Mann-Whitney U test. The result of significance test showed that intergroup comparison on mean by accident types made difference except rear-end accident type and by accident locations exit section only showed difference in significance level at 4-legged intersections, so the accident number have more where the U-turn is permitted than not. Response measures about the number of accidents were classified by whether accidents occurred and accident model were constructed using binomial logistic regression analysis method. The developed models show that the variables of conflict traffic, number of opposing lane are adopted as independent variable for both intersections. The variables of longitudinal grade for 3-legged signalized intersection and number of crosswalk for 4-legged signalized intersection at which the U-turn is permitted is adopted as independent variable only. These study results suggest that U-turn would be permitted at the intersection where the number of opposing lane is more than 3.5 each, the longitudinal grade of opposing road is upward flow and there is need to establish the U-turn traffic sign at signalized intersections.

• Journal of Korean Society of Transportation
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• v.24 no.2 s.88
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• pp.139-145
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• 2006

This paper develops pedestrian fatality models capable of producing the probability of pedestrian fatality in collision between vehicles and pedestrians. Probabilistic neural network (PNN) and binary logistic regression (BLR) ave employed in modeling pedestrian fatality pedestrian age, vehicle type, and collision speed obtained from reconstructing collected accidents are used as independent variables in fatality models. One of the nice features of this study is that an iterative sampling technique is used to construct various training and test datasets for the purpose of better performance comparison Statistical comparison considering the variation of model Performances is conducted. The results show that the PNN-based fatality model outperforms the BLR-based model. The models developed in this study that allow us to predict the pedestrian fatality would be useful tools for supporting the derivation of various safety Policies and technologies to enhance Pedestrian safety.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.391-398
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• 2019

In general, truck-involved crashes increase severity in terms of both injury level and crash impact level. Recently, although the frequency and fatality of truck-involved crashes in Korea are rising, their associative studies are very limited. Therefore, the objective of this study is to identify critical factors influencing on injury severity of truck-involved crashes on Korean freeway system. To carry out this objective, this study uses an ordered probit model (OPM) based on a 6-year crash dataset from 2012 to 2017. From the analysis, eight variables were found to have a great effect on injury severity: older driver, crash speed, rear-end collision, number of vehicles involved, drowsy driving, nighttime (0:00 to 6:00) driving, overturn or rollover, and vehicle's fire after crash. However, injury severity was less severe in crashes under snowy condition and crashes to traffic facilities (i.e., crash alone).

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.357-366
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• 2007

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.149-155
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• 2007

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.106-106
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• 2019

많은 도심의 하천들은 오염물질의 유입에 취약하다. 최근 신소재 공학 등 첨단산업이 발전하게 되면서 유해화학물질의 유입문제는 더욱 대두되고 있으며, 실제로 최근 유해화학물질 유입사고 발생건수가 늘어나고 있다. 특히 국내 취수량의 90%는 지표수에서 취수하고 있어, 하천오염사고는 직접적인 피해로 이어지게 된다. 따라서 이러한 사고에 대응하기 위하여 수환경에 유입된 유해물질의 거동 매커니즘을 반영한 수질해석이 필요하다. 수체 내에 유입된 유해화학물질은 기본적으로 흐름에 따른 이송 확산을 하며 흡 탈착, 휘발, 침전 부유, 생화학 반응과 같은 다양한 반응과 함께 혼합거동을 한다. 특히 소수성물질의 경우 용해된 상태뿐만 아니라, 유사에 흡착된 상태로 수체에 존재하게 된다. 결국 유해화학물질의 거동을 해석하기 위해서는 유체의 흐름 해석뿐만 아니라 수체에 존재하는 유사의 이송 또한 해석해야한다. 본 연구에서는 흐름해석을 위하여 서울대에서 개발한 흐름모형(HDM-2D)을 사용하였으며, 부유사 거동모의를 위해 부유사거동모형(STM-2D)을 개발하였다. 또한 유해화학물질의 거동모의를 위해 서울대에서 개발한 수질모형(CTM-2D)에 생성/소멸항을 추가하였으며 흐름모형과 부유사모형과의 연계를 통해 유해화학물질의 혼합거동 수치모형을 개발하였다. 각 반응항(흡 탈착, 휘발, 침전 부유, 생화학 반응)을 수치모형에 반영 시에는 보통 두 계(물-토양, 물-공기) 사이의 선형 물질교환으로 이해된다. 따라서 물질의 각 반응 별 평형농도와 물질교환속도계수를 추정식을 통해 산정하여 사용하게 된다. 하지만 각 기작이 반영유무에 따라 계산시간 및 필요입력변수가 늘어나게 되므로, 유해화학물질 유입사고와 같은 빠른 대처가 필요한 경우 각 반응 텀의 유의성을 판단하여 모형에 반영여부를 결정을 통해 경제적인 모의를 할 수 있어야 한다. 이에 따라 본 연구에서는 개발된 모형의 각 매개변수들의 민감도를 분석하고, 흐름조건 및 물질의 특성에 따른 반응항의 유의성을 판단하였다. 본 연구에서는 개발된 모형(부유사거동모형, 유해화학물질의 혼합거동모형)은 해석해 및 현장 데이터와 비교검증을 통해 개발을 완료하였으며, 각 반응항의 민감도 분석을 통해 매개변수의 임계값을 결정하였다.