PURPOSES : This study aims to identify the thresholds at which various factors affecting traffic crashes lead to actual traffic crashes METHODS : To verify the thresholds, we created scenarios and ran simulations with a combination of factors that affect traffic crashes. Lateral offset and minimum TTC were used to evaluate whether an incident occurred. RESULTS : In the first scenario, the most significant factor affecting traffic crashes is curvature, and it was found that the smaller the curvature(200 meters or less), the greater the deviation from the lane. And in the second scenario, especially the passenger car scenario, no accidents occurred when the curvature was greater than 90 meters and the speed was 40 km/h or less. The smaller the curvature and the higher the speed, the more accidents occurred. Similarly, in the bus scenario, no accidents occurred when the curvature was 120 meters or more and the speed was 30 km/h or less. Also, accidents tended to occur when the curvature was smaller and the speed was higher. CONCLUSIONS : Through this study, we derived the thresholds of factors that influence traffic crashes. The results are expected to help design and operate roads in the future and contribute to reducing traffic crashes.

목적 : 본 연구는 미국의 조건부 운전면허 관련 상세 운영 현황을 알아보고, 분석한 결과를 바탕으로 고령운전자 안전운전 을 위한 국내 조건부 운전면허 도입 시 참고가 될 수 있는 기초자료를 마련하고자 하였다. 또한, 운전재활전문가로서 작 업치료사의 조건부 운전면허에 대한 관심을 제고시키고자 하였다. 연구방법 : 미국자동차협회 교통안전재단(AAA Foundation for Traffic Safety)의 자료를 참고하여 미국의 고령운전자 사고율이 25% 이상에 해당하는 상위 4개 주인 켄터키, 미시시피, 몬태나, 와이오밍 주의 고령운전자 조건부 운전면허 관 련 현황을 정리하였다. 결과 : 본 연구에서는 미국 4개 주의 고령운전자의 운전면허 갱신, 고령운전자 조건부 운전면허 실시 및 제한 방법, 고령 운전자 조건부 운전면허 결정 판단 주체, 지역 현지 심사관(Local examiner)의 고령운전자 조건부 운전면허 결정과 관 련된 현황을 주로 파악해보았다. 조건부 운전면허 제한 유형으로는 고속도로 접근 제한과 거리 제한, 차량 장비 등이 있 었고 각 주의 공통적인 부분으로는 주간이나 낮에만 운전할 수 있도록 야간운전 제한 및 속도를 제한하고 있음을 파악하 였다. 결론 : 본 연구는 미국 4개 주의 고령운전자 조건부 운전면허제도 관련 현황 분석을 통해 우리나라 고령운전자 조건부 운 전면허제도 도입 시 참고 가능한 자료임에 의의가 있다. 앞으로 우리나라 고령운전자를 위한 국내 환경에 맞는 조건부 운전면허제도 도입과 관련된 기초자료로 활용될 수 있을 것으로 사료된다.

PURPOSES : There are significant differences in traffic accident rates depending on various road conditions and environments. However, the current traffic accident rates on national highways are classified relatively simply, and it is also difficult to accurately calculate the crash modification factor. Therefore, this study aimed to improve the traffic accident rates on national highways by presenting an algorithm for categorizing the traffic accident rates of national highway into four types (older and modern roads, and urban and rural roads). METHODS : The problems in the current rate of traffic accidents were derived, Traffic accident analysis system(TAAS) was used for the traffic accident data, and the road traffic volume statistical yearbook was used for the traffic volume data. After dividing the national highways into older and modern roads and urban and rural roads, the rates of traffic accidents were calculated and compared with the current accident rates. RESULTS : The accident rate of modern roads was found to be lower than that of older roads, and was lower in rural areas than in urban areas. From comparing the results of this study with Korea development institute(KDI) guidelines, older roads and urban roads exceeded the value in the KDI guideline, whereas the rates of modern roads and rural roads were lower than the KDI value. CONCLUSIONS : The accident rate accuracy was improved by subdividing the accident rates into four types. Therefore, it is expected that the accuracy and reliability of economic analysis on road projects will be improved.

해양사고 예방을 위해서는 사고의 원인과 결과에 대한 분석 및 진단뿐만 아니라, 사고의 발생 패턴과 변화 추이를 예측함으로 써 정량적 위험도를 제시할 필요성이 있다. 선박교통과 관련된 해양사고 예측은 선박의 충돌위험도 분석 및 항해 경로 탐색 등 선박교통 의 흐름에 관한 연구가 주로 수행되었으며, 해양사고의 발생 패턴에 대한 분석은 전통적인 통계 분석에 따라 제시되었다. 본 연구에서는 해양사고 통계 자료 중 선박교통관련 사고의 월별, 시간대별 발생 현황 데이터를 활용하여 해양사고 발생 예측 모델을 제시하고자 한다. 국내 해양사고 발생 현황 중 월별, 시간대별 데이터 집계가 가능한 1998년부터 2021년까지의 통계자료 중 선박교통 관련 데이터를 분류하 여 정형 시계열 데이터로 변환하였으며, 대표적인 인공지능 모델인 순환 신경망 기반 장단기 기억 신경망을 통하여 예측 모델을 구축하 였다. 검증데이터를 통하여 모델의 성능을 검증한 결과 RMSE는 초기 신경망 모델에서 월별 52.5471, 시간대별 126.5893으로 나타났으며, 관측값으로 신경망 모델을 업데이트한 결과 RMSE는 월별 31.3680, 시간대별 36.3967로 개선되었다. 본 연구에서 제안한 신경망 모델을 기 반으로 다양한 해양사고의 특징 데이터를 학습하여 해양사고 발생 패턴을 예측할 수 있을 것이다. 향후 해양사고 발생 위험의 정량적 제 시와 지역기반의 위험지도 개발 등에 관한 추가 연구가 필요하다.

PURPOSES : In this study, the factors affecting the severity of traffic accidents in highway tunnel sections were analyzed. The main lines of the highway and tunnel sections were compared, and factors affecting the severity of accidents were derived for each tunnel section, such as the tunnel access zone and tunnel inner zone. METHODS : An ordered probit model (OPM) was employed to estimate the factors affecting accident severity. The accident grade, which indicates the severity of highway traffic accidents, was set as the dependent variable. In addition, human, environmental, road condition, accident, and tunnel factors were collected and set as independent variables of the model. Marginal effects were examined to analyze how the derived influential factors affected the severity of each accident. RESULTS : As a result of the OPM analysis, accident factors were found to be influential in increasing the seriousness of the accident in all sections. Environmental factors, road conditions, and accident factors were identified as the main influential factors in the tunnel access zone. In contrast, accident and tunnel factors in the tunnel inner zone were found to be the influencing factors. In particular, it was found that serious accidents (A, B) occurred in all sections when a rollover accident occurred. CONCLUSIONS : This study confirmed that the influencing factors and the probability of accident occurrence differed between the tunnel access zone and inner zone. Most importantly, when the vehicle was overturned after the accident occurred, the results of the influencing factors were different. Therefore, the results can be used as a reference for establishing safety management strategies for tunnels or underground roads.

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

PURPOSES : For vehicle-alone accidents with a high mortality rate, it is necessary to analyze the factors influencing the severity of roadside fixed-object traffic accidents. METHODS : A total of 313 roadside fixed obstacle traffic accidents, variables related to fixed obstacles, and variables related to road geometry were collected. The estimation model was constructed with data collected using an ordinal probit regression model. RESULTS : Piers, vertical slopes, and distances between roads and objects were the primary causes of increased accident severity. CONCLUSIONS : Countermeasures, such as object removal, relocation, clear zones, frangibles, breakaway poles, etc., are required for accident-prone or dangerous points.

PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method. METHODS: In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models. RESULTS: The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with R2: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with R2: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model’s statistical significance. CONCLUSIONS: Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

PURPOSES: The purpose of this study is to develop a crash prediction model at signalized intersections, which can capture the randomness and uncertainty of traffic accident forecasting in order to provide more precise results. METHODS: The authors propose a random parameter (RP) approach to overcome the limitation of the Count model that cannot consider the heterogeneity of the assigned locations or road sections. For the model’s development, 55 intersections located in the Daejeon metropolitan area were selected as the scope of the study, and panel data such as the number of crashes, traffic volume, and intersection geometry at each intersection were collected for the analysis. RESULTS: Based on the results of the RP negative binomial crash prediction model developed in this study, it was found that the independent variables such as the log form of average annual traffic volume, presence or absence of left-turn lanes on major roads, presence or absence of right-turn lanes on minor roads, and the number of crosswalks were statistically significant random parameters, and this showed that the variables have a heterogeneous influence on individual intersections. CONCLUSIONS : It was found that the RP model had a better fit to the data than the fixed parameters (FP) model since the RP model reflects the heterogeneity of the individual observations and captures the inconsistent and biased effects.

PURPOSES: Traffic cameras have been installed to reduce traffic accidents. The effectiveness of traffic cameras has been proved by dozens of studies, but recently questions over its effectiveness have been raised by a series of studies. In this study, the effectiveness of traffic cameras was analyzed with a focus on different road environments. METHODS : The effectiveness of the traffic cameras was analyzed by extracting the occurence frequency before and after camera installation. The effect of reduction was analyzed comprehensively considering the installation position, monitoring direction, and surrounding environment of traffic cameras. RESULTS : The result of this study is as follows. First, the installation of cameras in an area with relatively low accidental traffic was more effective. Secondly, the effect of camera installation on car-to-pedestrian collisions was better than that of car-to-car collisions. Thirdly, accidents tended to occur more frequently when cameras were installed in front of the accident-prone owing to the negative spill-over effect. CONCLUSIONS: The result can be used to guide placement of traffic cameras. Moreover, the installation of cameras with consideration of the road environment is expected to contribute to a reduction in traffic fatalities.

최근 여성 운전자의 수가 점차 늘어감에 따라 여성운전자의 교통사고 발생건수 또한 증가하고 있다. 지난 5년 동안 국내 교통사고 발생건수는 감소해왔으나 여성운전자 교통사고 건수는 오히려 지속적으로 증가하고 있어 이에 대한 관심과 대책이 필요한 실정이다. 본 논문은 서울시 여성운전자 교통사고발생 지점을 시공간적으로 시각화하고, 분석하여 여성운전자 교통사고 예방에 도움이 되고자 하였다. 이를 위해 본 연구에서는 교통사고 발생 지점의 경위도좌표와 발생시간 정보를 포함하고 있는 2010년도 서울시 여성교통사고 데이터를 분석대상으로 하였다. 여성운전자 교통사고의 집중지역을 분석하기 위해 커널밀도분석, 핫스팟분석을 수행하였으며, 시공간 특성분석을 위해 시간대별 핫스팟 분석, 스페이스 타임 큐브 분석, 그리고 발생 핫스팟 분석을 수행하고 그 결과를 비교하였다. 마지막으로 여성운전자 교통사고 발생의 시공간적 집중 지역을 분석하고 요약한 뒤 이에 따른 시사점을 도출하였다.

PURPOSES :This study deals with traffic accidents involving trucks. The objective of this study is to develop a traffic accident model for trucks at roundabouts.METHODS :To achieve its objective, this study gives particular attention to develop appropriate models using Poisson and negative binomial regression models. Traffic accident data from 2007 to 2014 were collected from TAAS data set of road traffic authority. Thirteen explanatory variables such as geometry and traffic volume were used.RESULTS :The main results can be summarized as follows: (1) two statistically significant Poisson models (ρ2 = 0.398 and 0.435) were developed, and (2) the analysis revealed the common variables to be traffic volume, number of exit lanes, speed breakers, and truck apron width.CONCLUSIONS :Our modeling reveals that increasing the number of speed breakers and speed limit signs, and widening the truck apron width are important for reducing the number of truck accidents at roundabouts.

OBJECTIVES : The objective of this study is to develop a traffic accident model of a roundabout based on the type of land use. METHODS : The traffic accident data from 2010 to 2014 were collected from the“ traffic accident analysis system (TAAS)”data set of the Road Traffic Authority. A multiple linear regression model was utilized in this study to analyze the accidents based on the type of land use. Variables such as geometry and traffic volume were used to develop the accident models based on the type of land use. RESULTS : The main results are as follows. First, the null hypothesis that the type of land use does not affect the number of accidents is rejected. Second, four accident models based on the type of land use have been developed, which are statistically significant (high R2 values). Finally, the total entering and circulating volumes, area of the central island, number of speed breakers, mean number of entry lanes, diameter of the inscribed circle, mean width of the entry lane, area of the roundabout, bus stops, and number of circulatory roadways are analyzed to see how they affect the accident for each type of land use. CONCLUSIONS: The development of the accident models based on the type of land use has revealed that the accident factors at a roundabout are different for each case. Thus, more speed breakers in commercial areas and an inscribed circle of proper diameter in commercial and residential areas are determined to be important for reducing the number of accidents. Additionally, expanding the width of the entry lanes, decreasing the area of the roundabouts in residential areas, and reducing the conflict factors such as bus stops in green spaces are determined to be important.