• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.127-135
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• 2004

교차로 안전성 진단과 관련된 기존의 연구는 교차로 상에서 발생한 사고 자료에 기초하여 교차로 기하구조 요소, 교통량 및 신호운영방법 등과 관련된 요인을 변수로 사용하여 교통사고건수 예측모형 개발에 관한 연구가 대부분이다. 그러나, 분석하고자 하는 대상 교차로의 사고건수 예측모형을 개발하기 위해 필요한 교통사고 자료의 경우 단 기일에 걸쳐 획득되지 않으며 몇 년간의 사고 자료를 요구할 수도 있다. 이러한 자료를 이용하더라도 사고 발생 기간동안 교차로 사고에 영향을 미치는 요인(교차로 운영방법, 기하구조 등)이 변화될 수도 있다는 문제점을 지닌다. 이와 같은 이유로 교차로 안전성을 진단하는데 있어 기존 교통사고 자료는 언제나 절대적인 자료가 될 수 없다. 이에 대한 보완책으로, 3일에서 5일정도의 조사 자료만으로도 안전성 진단이 가능한 상충자료를 이용하여 교차로 안전성 진단을 할 수 있다. 본 연구는 기존사고 자료를 이용하여 사고 발생에 기인하는 여러 변수들을 교통사고심각도와의 상관관계를 분석하고, 상관관계가 높은 변수를 이용하여 신경망 사고심각도 예측모형을 개발하였으며, 모형 검증을 위해 다중회귀사고심각도 예측모형을 개발하여 비교 평가한 결과 신경망 사고심각도 예측모형의 예측력이 우수한 것으로 나타났다. 현장에서 조사된 상충자료를 신경망 사고심각도 예측모형에 적용하여 상충이 사고로 연결 될 경우 사고심각도를 예측하였으며, 예측된 사고심각도에 가중치를 부여하여 대상 교차로 위험우선순위를 결정한 결과 사고비용에 기초한 위험우선순위 결정법과 같은 순위의 결과를 도출하였다.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.315-322
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• 2007

신호교차로 교통사고는 90년대 이후 도시가 발달하고 산업이 고도화됨에 따라 교통 혼잡 문제와 함께 심각한 사회문제로 대두되고 있다. 특히 신호교차로의 교통사고는 인적요인, 차량요인, 환경적 요인 등이 복합적으로 작용하여 발생하는데, 교통량의 집중과 도로의 기하구조, 운전자 과실 등이 교통사고의 주요 인자로 작용하고 있다. 본 연구에서 교통사고 예측모형을 개발하기 위해서 2003년부터 2006년도까지 실제 경기도의 신호교차로에서 발생한 교통사고자료를 기초로 하였다. 구체적으로는 시내가 아닌 지방부 성격을 지닌 일반국도를 대상으로 하였다. 지방부 일반국도의 신호교차로 교통사고 분석에 단순통계분석과 다중회귀분석을 사용하였다. 사고와 관계가 높은 신호주기, 방향별 접근 교통량, 회전교통량 둥과 같은 도로, 교통, 운영조건들로 변수를 정하여 교통사고 예측모형을 도출하였다. 본 연구에서는 도로조건, 교통조건, 운영조건들과 사고와의 관계를 이용하여 경기도 일반국도의 신호교차로 교통사고예측모형을 개발하였고, 이는 지방부 성격을 지닌 교차로에 적용이 가능하다고 판단된다.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.207-218
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• 2001

As a city and industries are developed rapidly, a traffic accident and congestion take places on the road link become serious and it can be a large problem of the society in the future. Especially, most of the traffic accidents on the signalized intersection are caused by the human factor, vehicle and environmental factor mutually. The relation of the traffic accident and volume is acting on the outbreak of the traffic accident and the mistake of driver altogether as a major cause. The purpose of this paper is to develop a model for the forecasting of the traffic accident and to use research data gained to predict many traffic accidents. The data of this study were used with real one of the 73 areas of the four-legged signalized intersection in Kwang-ju city from 1996 to 1998 for three years to develop a model for the forecasting of the traffic accident. The statistical methods used in this paper are the principal component, regression and correlation analysis. We studied accident models to find out useful data from the statistics method and applied the data to the different area of the Choun-La province for the verification of the model. So, the result of this paper showed a reasonable model for the forecasting or the traffic accident and possibility of the model for simulating on real case. Finally, This study would be made of a study continually for the safe design and plan for the four-legged signalized intersection.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.39-56
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• 2011

This study proposed a traffic accident prediction model developed based on administrative districts of Seoul. The model was to find the relationship between accident rates and the representative land usage of the districts (development density) - the higher the development density (building floor area) is, the higher the traffic accident rate is. The findings showed that traffic accident statistics differ from (1) residential building floor area, (2) commercial building floor area and (3) business building floor area.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.1-15
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• 2023

In this study, the number of truck traffic accidents was predicted by using Poisson and negative binomial regression analysis to understand what factors affect accidents using expressway data. Significant variables in the truck traffic accident prediction model were continuous driving time, link length, truck traffic volume. number of bridges and number of drowsy shelters. The calculated LOSS rating was expressed on the national expressway network to diagnose the risk of truck accidents. This is expected to be used as basic data for policy establishment to reduce truck accidents on expressways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.145-154
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• 2012

This paper analyzed the characteristics of traffic accidents in all tunnels on nationwide freeways and selected some various independent variables related to accident occurrence in tunnels. The study aims to develop reliable accident forecasting models using the various dependent variables such as the number of accident (no.), no./km, and no./MVK. Finally, reliable multiple linear regression models were proposed in this paper. This study tested the validity verification of developed models through statistics such as $R^2$, F values, multicollinearity, residual analysis. The paper selected the accident forecasting models considering the characteristics of tunnel accidents and two models were finally proposed according to two groups of tunnel length. In the selected models, natural logarithm of ln(no./MVK) is used for the dependent variable and AADT, vertical slope, and tunnel hight are used for the independent variables. The reliability of two models was proved by the comparison analysis between field data and estimating data using RMSE and MAE. These models may be not only effective in evaluating tunnel safety under design and planning phases of tunnel but also useful to reduce traffic accidents in tunnels and to manage the traffic flow of tunnel.

• Journal of Korean Society of Transportation
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• v.14 no.4
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• pp.155-178
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• 1996

신호교차로 서비스수준은, 객관적으로 측정 할 수 있는 여러 가지 기준에 의해 결정될 수 있다. 예를 들면, 지체시간(Delay), 교통사고수(Number of Accident), 교통사고율(Accident Rate), 충돌수(Traffic Conflict), 그리고 교통사고에 노출된 차량수(Exposure)등이다. 지금까지는 1985 Highway Capacity Manual(HCM)에서 소개된 지체시간에 의한 서비스수준 결정방법이 널리 사용되어 왔다. 본 논문에서는 1985 HCM 방법의 중용성과 유용성에 대해 논하지 않고, 교통안전(Safety)에 의한 신호교차로 서비스수준 결정방법을 제시하였다. 교차로의 위험도(Degree of Intersection Hazard)를 예측하기 위해, 교통사고빈도 수가 가장 높은 두가지 교통사고 유형, 즉 좌회전추돌(Left-Tum)과 후미추돌(Rear-End) 예측 모형이 개발되었다. 여기서 첫째, 좌회전추돌 위험도를 예측하기 위하여 음지수 분포(Negative-Exponential Distribution)를 이용한 확률적 모형이 개발되었다. 둘째, 후미추돌 위험도를 예측하기 위하여 연속류 모형(Continuum Model)을 이용한 거시적 모형이 개발되었다. 개발된 두가지 모형을 이용하여 신호교차로 안전도를 예측하였으며 교차로 서비스수준이 안전도에 의해 결정되었다. 본 논문에서 제시된 교통안전에 의한 신호교차로 서비스수준 결정방법은 연동교차로를 제외한 독립교차로에만 적용이 된다.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.31-39
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• 2008

Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis, developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(D), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.123-135
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• 2007

The objective of this study is to analyze the relationship between traffic accidents occurring at trumpet interchange ramps according to accident type as well as the relevant factors that led to the traffic accidents, such as geometric design elements and traffic volumes. In the process of analysis of the distribution of traffic accidents, negative binomial distribution was selected as the most appropriate model. Negative binomial regression models were developed for total trumpet interchange ramps, direct ramps, loop ramps and semi-direct ramps based on the negative binomial distribution. Based upon several statistical diagnostics of the difference between observed accidents and predicted accidents with four previously developed models, the fit proved to be reasonable. Understanding of statistically significant variables in the developed model will enable designers to increase efficiency in terms of road operations and the development of traffic accident prevention policies in accordance with road design features.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.497-507
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• 2015

Nonrecurrent congestion of freeway was primarily caused by incident. The main cause of incident was known as a traffic accident. Therefore, accurate prediction of traffic incident clearance time is very important in accident management. Traffic accident data on freeway during year 2008 to year 2014 period were analyzed for this study. KNN(K-Nearest Neighbor) algorithm was hired for developing incident clearance time prediction model with the historical traffic accident data. Analysis result of accident data explains the level of accident significantly affect on the incident clearance time. For this reason, incident clearance time was categorized by accident level. Data were sorted by classification of traffic volume, number of lanes and time periods to consider traffic conditions and roadway geometry. Factors affecting incident clearance time were analyzed from the extracted data for identifying similar types of accident. Lastly, weight of detail factors was calculated in order to measure distance metric. Weight was calculated with applying standard method of normal distribution, then incident clearance time was predicted. Prediction result of model showed a lower prediction error(MAPE) than models of previous studies. The improve model developed in this study is expected to contribute to the efficient highway operation management when incident occurs.