• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.35-46
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• 2008

The purposes of this study are to analyze the characteristics and to develop the models of traffic accidents. In pursuing the above, this study gives particular attentions to developing the models(multiple linear, poisson and negative binomial regression) using the data of Cheongju and Cheongwon signalized intersections. The main results analyzed are as follows. First, the accident characteristics of rural area were defined by factor. Second, 4 accident models which are all statistically significant were developed. Finally, such the variables as $X_2$ and $X_{11}$ were evaluated to be specific variables which reflect the characteristics of rural area.

• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.570-576
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• 2017

Trucks take up more portions than cars on highways. Due to this, road use relatively diminish and it serves locally as a threatening factor to nearby drivers. Baggage car accident has distinct characteristics so that it needs the application of different analysis opposed to ordinary accidents. Accident prediction model, one of accident analyses, is used to predict the numbers of accident in certain parts, establish traffic plans as well as accident prevention methods, and diagnose the danger of roads. Thus, this study aims to apply the accident rate of baggage car on highways and calculate the correction factor to be put in the accident prediction models. Accident data based on highway was collected and traffic amounts and accident documents between 2014 and 2016 were utilized. The author developed an accident prediction model based on numbers of annual accidents and set mean annual and daily traffic amounts. This study intends to identify the practical accident prediction model on highway and present an appropriate solution by comparing the prediction model in accords with the accident rate between baggage cars.

• 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.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.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.47-56
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• 2008

Generally, accident exposure at intersections is relatively higher than that at roadway segments due to more possibility of merging, diverging, turning, crossing, and weaving maneuver. Furthermore, the traffic accident rate at intersections has been rapidly increasing since 1990's. Since there is more opportunity of conflict at unsignalized intersection, frequency and severity of traffic accident are more severe than signalized intersections. The purpose of the study is to analyze factors causing vehicle crashes and provide intersection design guidelines to improve intersection safety. For this study, vehicle to vehicle crash data of 116 rural 3 legs unsignalized were collected and field surveys were conducted for traffic and geometric conditions. Ordered probit models were developed to analyze the severity of crashes. It was found that weather, obstacles in minor roadsides, presence of major exclusive right lane, presence of major road crosswalk, difference between posted speed of major road and minor road, land-use around intersections, shoulder width of major road, ADT of major road are significant factors for intersection safety.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.53-63
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• 2008

Even though accident frequencies in roadway segments have been decreasing since 2000, there has been increasing the number of vehicle crashes at intersections. Due to this increase, safety problems at intersection recently started to be regarded as significant issues. The purpose of this study is to analyze the effects of road conditions, traffic operational conditions, and other influencing condition on intersection safety. Then a traffic accident frequency prediction model to evaluate the safety at intersections was developed based on the correlations between influencing factors and vehicle crashes. In this research, critically significant factors affecting vehicle crashes at rural four-legs signalized intersections were investigated. It was found that Poisson regression was the best fit method to developing a accident frequency modeling using the collected data in this study. Through this study, it was concluded that exclusive left turn lane, crosswalk, posted speed, lighting, angle, and ADT are significant influencing factors on the intersection safety.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.153-162
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• 2008

This study deals with the traffic accidents at the 4-legged signalized intersections in Cheong-ju. The purpose is to comparatively analyze the characteristics and models by the accident type using the data of 143 intersections. In pursuing the above, this study gives particular emphasis to modeling such the accidents as head on collision, rear end collision, side swipe, side right angle collision, and others. The main results are the followings. First, the overdispersion tests show that the negative binomial regression models are appropriate to the traffic accident data in the above contexts. Second, five accident models are developed, which are all analyzed to be statistically significant. Finally, the models are comparatively evaluated using the common variable(ADT) and type-specific variables.

• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.409-409
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• 1998

본 연구는 운전자 특성 및 교통사고 발생유형에 따른 사고 심각도(Severity)를 분석함으로써 교통사고를 유발키 쉬운 운전자 집단과 사고 발생시 위험도가 높은 사고유형을 규명하고자 하였다. 교통사고 위험집단 분석을 위한 운전자 특성은 성별, 차종, 연령 등을 대상으로 하였으며, 사고유형별 심각도 분석은 사고유형을 여덟 가지로 분류하고, 결과의 신뢰성 확보를 위하여 안전벨트 착용여부를 추가하여 상해정도와의 관계를 비교하였다. 로그-선형 모형 및 로짓 모형을 사용하여 카테고리 자료를 분석하였으며, 그 결과 위험집단 분석에서는 '20세 미만의 이륜차 운전자', '41세에서 50세까지의 택시운전자'가 가장 위험한 것으로, 또한 남자보다는 여자가 승용차, 택시, 이륜차 등에 관계되었을 때 위험한 것으로 조사되었다. 사고유형과 심각도와의 관계에서는 정면충돌 사고와 앞지르기 시 우회전시 사고가 기여위험도(Odds Multiplier)가 매우 높아 부상 또는 사망사고와 연계될 가능성이 큰 것으로 나타났다. 따라서 교통사고의 예방과 사고발생시의 심각도 경감을 위해서는 교통사고 취약계층으로 분석된 위험집단에 대한 교통안전 교육 및 홍보가 강화되어야 하고, 정면충돌 사고와 앞지르기 시 우회전시 발생하는 사고를 줄일 수 있는 방안이 연구되어야 할 것이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.577-585
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• 2010

Traffic accidents are caused by several factors such as drivers, vehicles, and road environment. It is necessary to investigate and analyze them in advance to prevent similar and repetitive traffic accidents. Especially, the human factor is most significant element and traffic accidents by drinking-driving caused from human factor have become social problem to be paid attention to. The study analyzes traffic accidents resulting from drinking-driving and the effects of driver's attributes and environmental factors on them. The study is composed as two parts. First, the log-linear model is applied to analyze that accidents by drinking or non-drinking driving associate with road geometry, weather condition and personal characteristics. Probability is tested for drinking-driving accidents relative to non-drinking drive accidents. The study analyzes probability differences between genders, between ages, and between kinds of vehicles through odds multipliers. Second, traffic accidents related to drinking are classified into property damage, minor injury, heavy injury, and death according to their severity. Heavy injury is more serious than minor one and death is more serious than heavy injury. The ordinal regression models are established to find effecting factors on traffic accident severity.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.17-25
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• 2009

The goal of this study is to develop Logistic regression model by accident location(entry section, exit section, inside intersection and pedestrian crossing section). Based on the accident data of Chungbuk Provincial Police Agency(2004$\sim$2005) and the field survey data, the geometric elements, environmental factor and others related to traffic accidents were analyzed. Developed models are all analyzed to be statistically significant(chi-square p=0.000, Nagelkerke $R^2$=0.363$\sim$0.819). The models show that the common factors of accidents are the traffic volume(ADT), distant of crossing and exclusive left turn lane, and the specific factors are the minor traffic volume(inside intersection model) and U-turn of main road(pedestrian crossing model). Hosmer & Loineshow tests are evaluated to be statistically significant(p$\geqq$0.05) except the entry section model. The correct classification rates are also analyzed to be very predictable(more than 73.9% to all models).