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.

PURPOSES : In this study, we quantitatively prove the rubber necking phenomenon for highway traffic accidents and develop a calculation model based on the influencing factors. METHODS : Vehicle detector speed data in the opposite direction to the accident point were used based on the accident data on highways over the past three years, and a comparative verification was performed between nearby vehicle detector data to verify the reliability of the data. Accordingly, a binomial logistic model, ordinal probit regression model, and multilinear regression model were developed to compare the orientation. RESULTS : There was a difference in the influencing factors based on the dependent variable, and the day of the week, vehicle type, weather, longitudinal slope, and median height had an effect. Through a regression analysis, an influence coefficient was derived to calculate the driving speed deceleration value by rubbernecking. The results of the model analysis proved that the speed reduction caused by rubbernecking was more evident during the daytime than at night, during weekends compared to weekdays, and the speed reduction was more obvious for heavy vehicles compared to other types of vehicles. It can also be concluded that longer clearance time, higher accident severity, and higher traffic volume affect traffic delay. To verify the data and model equation, the mean prediction bias (MPB) and mean absolute deviation (MAD) were calculated for hundred cases randomly extracted from the collected accident data. These results were excellent. CONCLUSIONS : It can be developed into a human-engineered model that reflects various road/facility conditions, such as highways, other lanes, general roads, and roads without a median strip. This study is meaningful as a basic study on the quantitative effect of rubber necking.

PURPOSES : This study analyzed explanatory variables, such as dangerous driving behaviors, in a negative binomial regression model, using the Digital Tachograph data of commercial vehicles, to assess the factors associated with freeway accidents. METHODS : Fixed parameter and random parameter negative binomial regression models were constructed using freeway accident data of commercial vehicles from January 2007 to July 2018 on the Gyeongbu Expressway from West Ulsan Interchange to Gimcheon Junction. RESULTS : Six explanatory variables (logarithm of average annual daily traffic, sunny, rainy, and snowy weather conditions, road curvature, and driving behaviors that included sudden stops) were found to impact the occurrence of freeway accidents significantly. Two of these variables (snowy weather conditions and sudden stops among dangerous driving behaviors) were analyzed as random parameters. These variables were shown as probabilistic variables that do not have a fixed impact on traffic accidents CONCLUSIONS : The variables analyzed as random parameters should be carefully considered when the freeway operating authorities plan an improvement project for highway safety.

PURPOSES : In this study, the installation of drowsy rest areas and accidents are analyzed. The factors that affected the accidents caused by drowsy drivers in rest areas are analyzed to improve the safety of rest areas. METHODS : By comparing and analyzing the installation status of the rest areas for drowsy drivers, the accident status were analyzed. The logistic regression model was used to analyze the factors that affect accidents in the drowsy rest area. RESULTS : Most rest areas were installed below the installation criteria. Several accidents occurred when the vehicle entered the drowsy rest area. These rest areas had a short entry ramp, and no safety facilities were installed. The logistic regression model showed that the risk of an accident is lowered when the deceleration lane is longer than 215 m. Additionally, the risk of an accident is lowered when the rest area is installed in the straight section or the curve section, wherein the curve radius is greater than 2 km. CONCLUSIONS : In this study, we evaluated the installation status of the rest areas for drowsy drivers by comparing installation elements. Most rest areas for drowsy drivers were installed at different lengths of the ramp. Some of these were installed on the slope or curved sections of the road. We analyzed the accident status and developed an accident modal using the logistic regression model to identify the factors that affect accidents. It will be necessary to analyze accidents in drowsy rest areas continuously to improve safety for drowsy drivers.

2011년∼2016년 고속도로 교통사고 정보 자료에 따르면 고속도로에서는 매년 9,200건 이상의 교통사고가 발생하고 있는 것으로 나타났다. 이 중 봉평터널 버스 6중 추돌사고(2016년), 둔내터널 부근 버스 추돌사고(2017년)와 같은 중차량으로 인한 대형 교통사고는 사회적으로 큰 반향을 불러일으키고 있다. 이에 본 연구에서는 고속도로 교통사고 정보 자료를 이용하여 사고등급, 사고원인, 사고유형, 사고차량수 등 다양한 측면에서 터널부 교통사고에 대한 현황 분석을 수행하였다. 고속도로 터널 연장에 따른 교통사고 현황을 분석하기 위하여 터널 연장 1km를 기준으로 1km 미만 터널, 1km 이상으로 구분하여 분석을 수행하였다. 고속도로 전체 터널 중 터널 내에서 교통사고가 발생한 터널을 구분해 보면 ‘1km 이상’터널은 160개소 ‘1km 미만’ 터널은 369개소로 나타났다. 교통사고 발생 터널의 평균연장은 고속도로 전체 터널의 평균연장에 비해서 긴 것으로 나타났다. 고속도로 터널 연장에 따른 교통사고 현황은 ‘1km 미만’ 터널의 사고 건수가 ‘1km 이상’ 터널에 비해서 많은 것으로 나타났다. 그러나 A급 교통사고 건수, 사망자 수, 부상자 수의 경우 ‘1km 이상’ 터널이 더 많은 것으로 분석되었다. 교통사고가 발생한 터널의 평균 교통사고 건수, 평균 사망자 수 평균 부상자수는 ‘1km 이상’ 터널이 ‘1km 미만’ 터널에 비해서 많았으며 교통사고 당 평균 사망자 수 및 평균 부상자 수 또한 많은 것으로 나타났다. 고속도로 노선별 터널부 교통사고 건수는 ‘순천완주선’이 총 278건으로 터널부 교통사고가 가장 많이 발생한 노선으로 나타났으며, 터널부 B급 이상 교통사고 건수의 경우 ‘중부내륙선’이 22건으로 타 노선에 비해서 심각한 교통사고의 발생 빈도가 월등히 높은 것으로 파악되었다. 고속도로 일반부와 터널부 교통사고 분석결과 일반부에 비해서 터널부의 A∼C급 사고 비율이 높은 것으로 나타났다. 사고원인 중 운전자 요인 비교결과 일반부는 ‘과속’이 가장 높은 비율을 차지한 반면에 터널부는 ‘주시태만’과 ‘졸음’이 높은 비율을 차지하는 것으로 나타났다. 사고차량 수별 교통사고 비율은 일반부의 경우 차량 ‘1대’의 단독 사고 비율이 74.1%, ‘2대’가 18.7%인 반면에 터널부는 일반부에 비해서 ‘2대’인 경우가 6.8%p 높은 것으로 나타났다. 고속도로 터널부 교통사고 중 사고원인차가 화물차, 승합차, 트레일러, 특수차량 등 중차량에 해당하는 교통사고 자료 분석결과 터널부 교통사고 중 B급 이상 사고 비율은 3.5%인 반면에 중차량 교통사고의 비율은 5.6%로 중차량으로 인한 사고의 피해 정도가 더 큰 것으로 나타났다. 사고원인의 경우 일반부 교통사고 및 터널부 일반 교통사고와 달리 터널부 중차량 교통사고의 경우 ‘차량요인’에 의한 교통사고가 ‘기타요인’에 비해서 높은 비율을 차지하는 것으로 나타났다. 사고 차량수는 터널부 일반 교통사고에 비해서 사고차량 수 ‘1대’의 비율은 3.2%p 감소한 반면 ‘2대’의 비율은 1.9%p 상승한 것으로 나타났다. 고속도로 터널부 중차량 교통사고의 연평균 치사율은 5.72%로 터널부 일반 교통사고에 비해서 2.65%p 높은 것으로 나타났으며 사고건당 부상자수 또한 연평균 0.1명 높게 나타났다

PURPOSES: This study was initiated to estimate the benefits from the campaign to prevent drowsy driving crashes on expressways. The study was conducted by the Korea Expressway Corporation using a contingent valuation method. METHODS : First, a questionnaire was designed for a preliminary survey. From the survey’s results, the initial willingness to pay for the campaign was determined by averaging different amounts of payments chosen under virtual scenarios in the survey. The willingness to pay data was used to find a first bid price for the open-ended method used for the second survey. After that, a primary questionnaire was designed and conducted using a single dichotomous choice question (SDBCQ). Drivers at expressway resting areas were asked their willingness to pay for the campaign. Based on statistical analysis using data collected from the second survey, the mean willingness to pay was estimated using a probability utility function. Finally, the benefit from the campaign was calculated using the estimated willingness to pay and accident data on expressways. CONCLUSIONS : Based on the result from the contingent valuation method, the benefit from the campaign to prevent drowsy driving crashes was estimated to be 170.6 won per expressway trip. The benefit is to be paid as an additional toll. In addition, the traffic crash cost estimate is about 2,209,680,000 won less than the cost during the same period in 2014.

PURPOSES : Low visibility caused by dark surroundings at nighttime affects the likelihood of accidents, and various efforts, such as installing road safety facilities, have been made to reduce accidents at night. Despite these efforts, the nighttime severity index (SI) in Korea was higher than the daytime SI during 2011-2014. This study determined the factors affecting daytime and nighttime accident severity through a discriminant analysis. METHODS: Discriminant analysis. RESULTS: First, drowsiness, lack of attention, and lighting facilities affected both daytime and nighttime accident severity. Accidents were found to be caused by a low ability to recognize the driving conditions and a low obstacle avoidance capability. Second, road conditions and speeding affected only the daytime accident severity. Third, failure to maintain a safe distance significantly affected daytime accident severity and nonsignificantly affected nighttime accident severity. The majority of such accidents were caused by rear-end collisions of vehicles driving in the same direction; given the low relative speed difference in such cases, the shock imparted by the accidents was minimal. CONCLUSIONS: Accidents caused by a failure to maintain a safe distance has lower severity than do accidents caused by other factors.

도로 안전에는 다양한 요인들이 복합적으로 작용한다. 이러한 요인들 중 미끄럼 저항은 도로 포장면과 타이어 사이의 마찰 저항을 말하며, 도로 안전에 매우 중요한 요소이다. 비 오는 날은 시야확보가 어렵고, 미끄러운 노면 때문에 조향과 제동에 어려움을 겪게 되어 사고위험성이 높아지게 된다. 비 오는 날 교통 사고의 위험성은 교통사고 100건당 사망자수인 치사율을 보면 알 수 있는데, 교통안전정보관리시스템의 통계에 의하면 2012~2014년의 교통사고 중 맑은 기상상태에서는 치사율이 2.16%이지만 비, 눈 상태의 치사율은 각각 2.76, 2.63%로 높게 집계되었다. 또한 도로교통공단의 통계에 따르면 2009~2011년 고속 국도의 야간 치사율은 10.6명으로 도로종류 중에서 가장 높은 것으로 나타났다. 따라서 본 연구에서는 고 속도로의 노면의 상태(건조, 젖음)와 시간(심야, 첨두, 비첨두)을 고려하여 고속도로의 미끄럼 저항 관리 기준 방안 제시를 위하여 고속도로 교통사고와 미끄럼 저항간의 관계를 누적사고건수비율/누적도로연장 비율을 평가지수로 이용하여 경부고속도로와 영동고속도로를 중심으로 분석하였다. 교통사고와 미끄럼 저항간의 관계 분석을 통하여 포장종류, 노면상태, 시간에 따라 누적사고건수비율/ 누적도로연장비율이 달라지는 것을 다음과 같이 확인하였다. 경부, 영동 고속도로 모두 미끄럼 저항이 낮 을 때 평가지수가 높은 것으로 분석되었다. 또한 젖은 노면 상태에서 평가지수가 높게 나타났다. 향후 본 연구결과를 토대로 고속도로 미끄럼 저항 관리기준을 합리적으로 수립할 수 있는 방안을 마련할 계획이 며, 고속도로 안전관리에 단계별 관리기준을 제공하는데 기여할 수 있을 것으로 판단된다.

PURPOSES: The objective of this study is to analyze factors affecting traffic accident severity for determining countermeasures on freeway climbing lanes. METHODS : In this study, an ordered probit model, which is a widely used discrete choice model for categorizing crash severity, was employed. RESULTS: Results suggest that factors affecting traffic accident severity on climbing lanes include speed, drowsy driving, grade of uphill 3%, gender (male offender and male victim), and cloud weather. CONCLUSIONS : Several countermeasures are proposed for improving traffic safety on freeway climbing lanes based on the analysis of crash severity. More extensive analysis with a larger data set and various modeling techniques are required for generalizing the results.

PURPOSES : The bridge section of the expressway has a worse driving environment than the general section. However, traffic safety countermeasures are focused only on the bridge section. Traffic safety countermeasures on the section before entry to the bridge and the section after exit from the bridge are applied only when the bridge has a long-span section. Accordingly, this study will verify the necessity of extending the application of traffic safety countermeasures to areas that are affected by the bridge. METHODS: This study determines the areas that are affected by the bridge as well as the areas that are affected by locations with frequent traffic accidents and suggests the risk factors by affected areas through canonical discriminant analysis. For the analysis, traffic accident data for 3 years, which occurred on bridge sections in six major expressway lines, were used. RESULTS: The numbers of traffic accidents were 469 before the bridge, 281 on the bridge, and 468 after the bridge. The variables that have impact on the seriousness of accidents are as follows: speeding, excess manipulation of the steering wheel, and failure to secure safety distance for accidents that occurred before the bridge section; speeding, excess manipulation of the steering wheel, and dozing off for accidents that occurred on the bridge; and speeding and failure to secure safety distance for accidents that occurred after the bridge section. CONCLUSIONS : Areas affected by the bridge show higher accident rates than the bridge section; therefore, imposing traffic safety countermeasures on the integrated section of the bridge and the affected areas is required. It is believed that the results suggested in this study could be effectively used in the prevention of traffic accidents by imposing custom-made safety countermeasures for each section.

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.

현재 교통사고 사망자수 줄이기를 위한 다양한 노력이 이루어지고 있으며, 이러한 노력의 결실을 맺기 위한 한 방편으로 교통사고취약구간에 대한 집중된 개선노력 및 투자가 요구되고 있다. 이러한 관점에서 본 연구는 고속도로 본선구간을 대상으로 사고유형별 교통사고취약구간 선정을 목적하고 있다. 이를 위하여 도로교통공단 교통사고분석시스템(TAAS)의 2007년에서 2013년까지 최근 7년간 고속도로 본선에서 발생한 총 21,724건을 대상으로 6가지 사고유형별로 구분된 데이터셋을 ArcMap을 활용하여 구 축한다. 교통사고취약구간 선정은 커널밀도를 활용한 KDE(Jernel Density Estimation)법을 활용하여 실 시한다. 기존 연구와의 차이점은 보다 정밀한 분석을 위하여 노선별 방향을 구분하여 접근함으로써 분석결과의 공동데이터로서의 활용도를 높였으며, 방대한 데이터셋과 더불어 사고유형별 접근을 통해 분석의 다양성 을 높였다는 점이다. 한편 KDE법을 활용하여 도출된 사고유형별 교통사고취약구간의 기하구조 및 교통특성 분석을 통하여 사고유형별로 달라질 수 있는 도로환경 요인에 대한 분석을 실시한다.

PURPOSES : This study aims to draw differences between primary and secondary crashes by comparing crash characteristics and to identify the unique characteristics of secondary crashes for making better effective countermeasures to reduce secondary crashes. METHODS : The characteristics of secondary crashes were compared to those of primary crashes through a two sample proportional test (z-test). RESULTS : The results showed that vehicle-to-vehicle crashes and vehicle-to-person crashes are dominant crash types in secondary crashes. Compared to primary crashes, secondary crashes were likely to occur during nighttime. With respect to season and weather, the proportion of secondary crashes occurred during winter and in snowy weather is relatively higher than that of primary crashes. The main causes of primary crashes were found to be drowsiness, speeding, and exaggerated steering control, whereas main factors affecting the occurrence of secondary crashes were negligence of keeping eyes forward and no keeping a safe distance as expected. CONCLUSIONS : The characteristics affecting the occurrence of secondary crashes are different from those of primary crashes, indicating that proper countermeasures should be established to prevent the occurrence of secondary crashes on highways.

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.

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

교통사고 발생시점부터 사고처리가 완료된 시점까지를 돌발상황 지속시간(Incident Duration Time)이라고 정의하는데, 이를 단축시켜야만 교통사고 피해를 최소화할 수 있다. 본 연구는 고속도로 교통사고를 대상으로 돌발상황 지속시간에 영향을 주는 요인들을 찾아내기 위한 모형을 개발하였다. 모형은 모든 사고자료에 포함한 통합 모형(모형1)과 일반구간, 교량, 터널 등 교통사고 장소별로 구분하여 분석한 세부 모형(모형 2, 3, 4) 등 모두 4개의 모형을 구축하였다. 분석 결과, 교통사고가 발생한 장소에 따라 돌발상황 지속시간에 다른 영향을 주는 것으로 나타났으며, 현장 처리를 위한 작업차량 도착시간이 가장 민감한 요인으로 분석되었다. 또한 차-차 사고, 화물차에 의한 사고, 야간 사고, 주말 사고 등의 시사점 있는 요인들을 찾아냈다. 이러한 연구결과는 향후 교통사고 관리대책을 수립하는 데 있어 중요한 판단지표로 활용이 가능할 것으로 판단된다.