PURPOSES : This study aimed to identify factors affecting the duration of traffic incidents in tunnel sections, as accidents in tunnels tend to cause more congestion than those on main roads. Survival analysis and a Cox proportional hazards model were used to analyze the determinants of incident clearance times. METHODS : Tunnel traffic accidents were categorized into tunnel access sections versus inner tunnel sections according to the point of occurrence. The factors affecting duration were compared between main road and tunnel locations. The Cox model was applied to quantify the effects of various factors on incident duration time by location. RESULTS : Key factors influencing mainline incident duration included collision type, driver behavior and gender, number of vehicles involved, number of accidents, and post-collision vehicle status. In tunnels, the primary factors identified were collision type, driver behavior, single vs multi-vehicle involvement, and vehicles stopping in the tunnel after collisions. Incidents lasted longest when vehicles stopped at tunnel entrances and exits. In addition, we hypothesize that incident duration in tunnels is longer than in main roads due to the reduced space for vehicle handling. CONCLUSIONS : These results can inform the development of future incident management strategies and congestion mitigation for tunnels and underpasses. The Cox model provided new insights into the determinants of incident duration times in constrained tunnel environments compared to open main roads.

PURPOSES : The main purpose of this study is to identify directions for improvement of triangular islands installation warrants through analysis of the characteristics of crashes and severity with and without triangular islands on intersections. METHODS : The data was collected by referring to the literature and analyzed using statistical analysis tools. First, an independence test analyzed whether statistically significant differences existed between crashes depending on the installation of triangular islands. As a result of the analysis, individual prediction models were developed for cases with significant differences. In addition, each crash factor was derived by comparison with each model. RESULTS : Significant differences appeared in the "crash frequency of serious or fatal" and "crash severity" owing to the installation of triangular islands. As a result of comparing crash factors through the individual models, it was derived that the differences were dependent on the installation of the triangular islands. CONCLUSIONS : As a result of reviewing previous studies, it is found that improving the installation warrants of triangular islands is reasonable. Through this study, the need to consider the volume and composition ratio of right-turn vehicles when installing a triangular island was also derived; these results also need to be referred to when improving the triangular island installation warrants.

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 : This study was conducted to develop a traffic accident prediction model using traffic accident data and management and service evaluation data on bus companies in Busan, and to determine the possibility of establishing customized traffic accident prevention measures for each company. METHODS: First, we collected basic data on the characteristics of urban bus traffic accidents and conducted basic statistical analysis. Then, we developed traffic accident prediction models using Poisson regression and negative binomial regression to examine the characteristics of major items of management and service evaluation affecting traffic accidents. RESULTS : The Poisson regression model showed overdispersion; hence, the negative binomial regression model was selected. The results of the traffic accident prediction model developed using negative binomial regression are acceptable at 95% confidence level (a = 0.05). CONCLUSIONS : The traffic accident prediction model indicates that the management of the traffic record system and internal and external management items in service evaluation have a significant effect on the reduction of traffic accidents. In particular, because human factors are the main cause of traffic accidents, bus traffic accidents are expected to greatly decrease if drivers' dangerous driving behaviors are effectively controlled by bus companies.

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.

Even though cars have a good effect on modern society, traffic accidents do not. There are traffic laws that define the regulations and aim to reduce accidents from happening; nevertheless, it is hard to determine all accident causes such as road and traffic conditions, and human related factors. If a traffic accident occurs, the traffic law classifies it as ‘Negligence of Safe Driving’ for cases that are not defined by specific regulations. Meanwhile, as Korea is already growing rapidly elderly population with more than 65 years, so are the number of traffic accidents caused by this group. Therefore, we studied predictive and comparative analysis of the number of traffic accidents caused by ‘Negligence of Safe Driving’ by dividing it into two groups : All-ages and Elderly. In this paper, we used empirical monthly data from 2007 to 2015 collected by TAAS (Traffic Accident Analysis System), identified the most suitable ARIMA forecasting model by using the four steps of the Box-Jenkins method : Identification, Estimation, Diagnostics, Forecasting. The results of this study indicate that ARIMA (1, 1, 0)(0, 1, 1)12 is the most suitable forecasting model in the group of All-ages; and ARIMA (0, 1, 1)(0, 1, 1)12 is the most suitable in the group of Elderly. Then, with this fitted model, we forecasted the number of traffic accidents for 2 years of both groups. There is no large fluctuation in the group of All-ages, but the group of Elderly shows a gradual increase trend. Finally, we compared two groups in terms of the forecast, suggested a countermeasure plan to reduce traffic accidents for both groups

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.

PURPOSES : The purpose of this study was to develop safety performance functions (SPFs) that use zero-inflated negative binomial regression models for urban intersections in central business districts (CBDs), and to compare the statistical significance of developed models against that of regular negative binomial regression models. METHODS : To develop and analyze the SPFs of intersections in CBDs, data acquisition was conducted for dependent and independent variables in areas of study. We analyzed the SPFs using zero-inflated negative binomial regression model as well as regular negative binomial regression model. We then compared the results by analyzing the statistical significance of the models. RESULTS : SPFs were estimated for all accidents and injury accidents at intersections in CBDs in terms of variables such as AADT, Number of Lanes at Major Roads, Median Barriers, Right Turn with an Exclusive Turn Lane, Turning Guideline, and Front Signal. We also estimated the log-likelihood at convergence and the likelihood ratio of SPFs for comparing the zero-inflated model with the regular model. In he SPFs, estimated log-likelihood at convergence and the likelihood ratio of the zero-inflated model were at -836.736, 0.193 and -836.415, 0.195. Also estimated the log-likelihood at convergence and likelihood ratio of the regular model were at -843.547, 0.187 and -842.631, 0.189, respectively. These figures demonstrate that zero-inflated negative binomial regression models can better explain traffic accidents at intersections in CBDs. CONCLUSIONS : SPFs that use a zero-inflated negative binomial regression model demonstrate better statistical significance compared with those that use a regular negative binomial regression model.

창조경제 정부에서 교통안전 선진화를 국정과제로 추진하고 있을 만큼 도로안전은 사회적 주요 관심대 상이다. 그러나 현재 우리나라에서는 도로안전사업 대상 지역의 선정기준이 되는 도로안전 취약 및 위험 구간을 도출하는 방법이 객관적으로 마련되어 있지 않다. 따라서 이러한 국내 상황을 극복하기 위해서는 도로 안전성 여부를 과학적으로 분석할 수 있는 평가기법 개발이 필요하다. 이를 위한 최적의 방안으로는 한국 현지 도로 여건에 맞는 사고예측모형을 개발하는 것이다. 그러나 사고예측모형 개발에 필요한 모형 입력 자료를 생성하기 위해서는 현장조사와 DB 구축 등에 많이 비용이 소요된다. 따라서 본 연구에서는 사고예측모형 개발에 필요한 자료 중 도로 기하구조 요소(곡선반경 등)를 수집하 기 위해 직접 현장조사를 하지 않고 엑셀 형태로 작성된 도로대장을 활용할 수 있는 방안에 대해 연구하 였다. 또한 사고예측모형을 공간정보 기반으로 개발하여 Network Screening과 개선대안 효과분석 등의 작업을 직관적이며 효과적으로 수행하고 사고예측을 정확하게 판단할 수 있는 장점을 확보하기 위하여 모 형입력 자료를 공간정보 DB로 구축하는 방안을 도출하였다. 따라서 본 연구에서는 임의로 설정한 기준점에서부터의 누적거리를 이용하여 시설물 위치와 도로 기하 구조 정보를 표현하는 도로대장을 도로망 수치지도(교통주제도)를 따라 선형 객체의 공간정보로 변환할 수 있는 기능을 개발하였다. 또한 사고발생 자료의 경우 수집된 자료의 지구좌료를 이용하여 점형 객체의 공간정보를 생성하였으며, 교통량조사 자료는 동일한 교통량으로 구분되는 선형 객체의 공간정보를 생성 하였다. 본 연구에서 제시한 모형 입력 자료 생성 방법은 교통주제도의 차로수에 따라 도로중심선을 1차 분할 한 후, 공간정보로 생성된 종단경사, 곡선반경, 중앙분리대 등으로 도로중심선을 2차 분할하였으며, 이를 토대로 GIS 공간분석을 수행하여 접도구역(면형객체), 사고위치(점형객체), 교통량(선형객체), 가로등의 정보를 분할된 동질구간에 매핑하는 방안을 도출하였다. 본 연구를 통하여 교통사고, 교통량 및 도로대장 자료를 모형 입력 자료 형식에 맞게 공간정보 DB 형태 로 구축하고, 이를 통해 GIS의 공간분석을 수행함으로서 정확한 사고건수 추출, 동질성 구간에 대한 체계 적인 분석과 과학적인 분할 등을 매우 신속하고 다양하게 시뮬레이션을 할 수 있음을 확인할 수 있었다.

우리나라의 교통사고는 매년 늘어나는 추세이며, 근본적인 교통사고의 감소를 위한 연구와 대책 마련 이 필요하다. 기존에 다수 진행되었던 차량여건이나 사고건수 연구는 단편적인 지표이기 때문에 복합적인 사고 예방과 해결에 어려움이 있다. 현재 사고밀도모형의 연구가 한정적이기 때문에 전국적으로 모형을 적용하기에는 다소 어려움이 있어 보인다. 이 연구는 심층적이고 다양한 교통 특성과 인구지표를 전국에 반영할 수 있는 교통사고 밀도 모형을 개발하는데 그 목적이 있다. 이 연구의 대상의 되는 자료는 전국 460개의 광역시 및 시･군구를 하나의 존으로 선정하여 2008~2015 년까지 발생한 교통사고자료이다. 이 연구에서는 다양한 독립변수와 그에 따른 종속변수 사이의 관계를 도출하고, 적합한 모형을 개발하기 위해 다중회귀분석을 사용했다. 종속변수는 사고밀도로 정하고, 독립 변수는 인구밀도, 경제활동인구, 1인가구수, 고령인구수 등과 같은 인구 기반지표와 토지이용 및 사회경 제적인 지표들로 정하였다. 결정된 변수들의 상관분석 및 F검정을 통해 모형의 적합도와 유의성 분석을 통해 최종적으로 가장 신뢰성 있는 교통사고밀도 모형을 결정한다. 중점적인 관련 변수로는 인구밀도, 경 제활동인구, 고령인구수, 상업면적, 장애인비율 등과 같은 변수가 양의 상관관계를 가질 것으로 예상되 고, 반대로 속도방지시설과 같은 변수들은 음의 상관관계일 것으로 예측된다. 개발된 모형을 통해 결과적 으로, 전국 시･군구 단위의 설명력있는 모형 개발과 이를 통한 정책평가나 장래의 계획에도 활용할 수 있 을 것으로 기대된다.

PURPOSES: This study deals with the traffic accidents classified by the traffic analysis zone. The purpose is to develop the accident density models by using zonal traffic and socioeconomic data. METHODS : The traffic accident density models are developed through multiple linear regression analysis. In this study, three multiple linear models were developed. The dependent variable was traffic accident density, which is a measure of the relative distribution of traffic accidents. The independent variables were various traffic and socioeconomic variables. CONCLUSIONS : Three traffic accident density models were developed, and all models were statistically significant. Road length, trip production volume, intersections, van ratio, and number of vehicles per person in the transportation-based model were analyzed to be positive to the accident. Residential and commercial area ratio and transportation vulnerability ratio obtained using the socioeconomic-based model were found to affect the accident. The major arterial road ratio, trip production volume, intersection, van ratio, commercial ratio, and number of companies in the integrated model were also found to be related to the accident.

PURPOSES : This study tries to develop the accident models of 4-legged signalized intersections in Busan Metropolitan city with random parameter in count model to understanding the factors mainly influencing on accident frequencies. METHODS: To develop the traffic accidents modeling, this study uses RP(random parameter) negative binomial model which enables to take account of heterogeneity in data. By using RP model, each intersection’s specific geometry characteristics were considered. RESULTS : By comparing the both FP(fixed parameter) and RP modeling, it was confirmed the RP model has a little higher explanation power than the FP model. Out of 17 statistically significant variables, 4 variables including traffic volumes on minor roads, pedestrian crossing on major roads, and distance of pedestrian crossing on major/minor roads are derived as having random parameters. In addition, the marginal effect and elasticity of variables are analyzed to understand the variables’impact on the likelihood of accident occurrences. CONCLUSIONS :This study shows that the uses of RP is better fitted to the accident data since each observations’specific characteristics could be considered. Thus, the methods which could consider the heterogeneity of data is recommended to analyze the relationship between accidents and affecting factors(for example, traffic safety facilities or geometrics in signalized 4-legged intersections).

PURPOSES : The purposes of this study are to compare the day and night characteristics and to develop the models of traffic accidents. in Rural Signalized Intersections METHODS : To develop day and night traffic accident models using the Negative Binomial Model, which was constructed for 156 signalized intersections of rural areas, through field investigations and casualty data from the National Police Agency. RESULTS : Among a total of 17 variances, the daytime traffic accident estimate models identified a total of 9 influence factors of traffic accidents. In the case of nighttime traffic accident models, 11 influence factors of traffic accidents were identified. CONCLUSIONS: By comparing the two models, it was determined that the number of main roads was an independent factor for daytime accidents. For nighttime accidents, several factors were independently involved, including the number of entrances to sub-roads, whether left turn lanes existed in major roads, the distances of pedestrian crossings to main roads and sub-roads, lighting facilities, and others. It was apparent that if the same situation arises, the probability of an accident occurring at night is higher than during the day because the speed of travel through intersections in rural areas is somewhat higher at night than during the day.

교통사고는 2013년 한 해 동안 총 215,354건이며, 최근 5년간 교통여건 및 교통안전정책의 발전으로 평균 1.84%로 감소하는 추세에 있다. 그러나 OECD 통계에 따르면 우리나라는 인구 10만 명당 교통사고 발생건수가 2012년 기준으로 OECD평균인 310.4건에 비해 약 1.4배 많은 447.3건이 발생하는 실정이다. 이는 교통사고가 아직 심각한 문제로 자리 잡고 있으며, 교통사고를 줄이기 위한 근본적인 원인 규명과 대안이 필요하다. 기존 진행되어 왔던 교통사고에 대한 연구는 단순히 인적요인, 차량요인, 도로환경 요 인과 같은 점, 선적인 여건들을 중점으로 연구가 대부분이었다. 교통사고를 줄일 수 있는 근본적이 해결 책을 마련하기 위해서는 다양한 여건들이 반영되어야 한다. 이 점에 착안하여 본 연구는 다양한 여건들을 아우르는 지역적 특성과 함께 복합적인 요인의 관계를 분석하는데 목적이 있다. 연구를 수행하기 위해 공 간적 범위 대상지를 청주시로 하고, 면적인 차원에서의 토지이용변수를 활용하여 존별 특성을 반영한 교 통사고 모형을 개발하는 것이다. 이를 위해 청주시의 30개의 행정동을 존으로 구분하여 각 존별 토지이용 과 교통여건, 사회경제적 특성을 변수로 활용하여 다중선형회귀분석을 실시하였다.

PURPOSES : The objective was to develop the advanced method which could not explain each observation’s specific characteristic in the present negative binomial method that results in under-estimation of the standard error(t-value inflation) and affects the confidence of whole derived results. METHODS : This study dealt with traffic accidents occurring within interchange segment on highway main line with RPNB(Random Parameter Negative Binomial) method that enables to take account of heterogeneity. RESULTS : As a result, AADT and lighting installation type on the road were revealed to have random parameter and in terms of other geometric variables, all were derived as fixed parameter(same effect on every segment). Also, marginal effects were adapted to analyze the relative effects on traffic accidents. CONCLUSIONS : This study proves that RPNB method which considers each observation’s specific characteristics is better fitted to the accident data with geometrics. Thus, it is recommended that RPNB model or other methods which could consider the heterogeneity needs to be adapted in accident analysis.

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

PURPOSES: Using the collected data for crash, traffic volume, and design elements on ramps between 2007 and 2009, this research effort was initiated to develop traffic crash prediction models for expressway ramps. METHODS: Three negative binomial regression models and three zero-inflated negative binomial regression models were developed for individual ramp types, including direct, semi-direct and loop, respectively. For validating the developed models, authors compared the estimated crash frequencies with actual crash frequencies of twelve randomly selected interchanges, the ramps of which have not been used for model developing. RESULTS: The results show that the negative binomial regression models for direct, semi-direct and loop ramps showed 60.3%, 63.8% and 48.7% error rates on average whereas the zero-inflated negative binomial regression models showed 82.1%, 120.4% and 57.3%, respectively. CONCLUSIONS: Conclusively, the negative binomial regression models worked better in traffic crash prediction than the zero-inflated negative binomial regression models for estimating the frequency of traffic accidents on expressway ramps.