결빙(Black Ice)은 도로 포장체 표면의 균열 등에 스며든 습기나 눈, 그리고 차량 주행 중 발생하는 타이어 분진 및 배 기가스 등의 영향으로 인해 도로 표면과 유사한 색상의 얇은 얼음막이 형성되는 현상을 의미한다(Cho et al., 2021). 도로 노면이 결빙 상태일 경우, 평균 미끄럼 저항 계수는 건조 노면의 약 30% 수준으로 크게 낮아진다(Lee et al., 2024). 또 한, 결빙은 도로 표면과 색상이 유사하여 운전자가 노면 상태를 즉각적으로 인지하기 어렵고, 이에 따라 제동이나 회피 를 위한 충분한 시간을 확보하기 어렵다. 최근 5년간 발생한 서리·결빙 노면 교통사고의 치사율(사고 100건당 사망자 수) 은 2.69명으로, 이는 건조 노면 교통사고 치사율의 약 2배, 습윤 노면의 1.3배 수준에 해당한다(KoROAD, 2024). 이러한 위험성을 고려하여 국토교통부는 2020년 전국 고속국도 및 일반, 위임국도를 대상으로 403개 구간을 결빙 취약 구간으로 지정하였으며, 이후 464개소로 확대하여 자동염수분사시설, 그루빙(Grovving), 결빙주의표지판 등 안전시설을 확충하여 결빙사고를 집중적으로 관리하고 있다(MOLIT, 2020; BAI 2021). 하지만, 결빙사고 발생건수는 2020년 524건, 2021년 1,204건, 2022년 1,042건으로 증가추세를 보이고 있어, 결빙 취약 구간의 평가 적절성과 실효성에 대한 검토 필요성이 대 두되고 있다(KoROAD, 2024). 본 연구에서는 최근 10년 고속국도에서 발생한 결빙사고와 결빙사고 영향인자를 Random Forest Algorithm으로 분석하 여 도로 구간별 결빙사고 위험도를 평가하였다. 국가교통정보센터의 노드·링크(Node·Link) 체계를 기반으로 전국 고속국 도의 동절기 기상, 기하구조, 교통량 등 결빙사고 영향인자를 구간별로 수집하였다. 각 구간은 최근 10년 결빙사고 데이 터를 통해 결빙사고 발생구간과 비발생 구간으로 분류하였다. 구간별 수집한 결빙사고 영향인자를 독립변수, 사고발생유 무를 종속변수로하여 알고리즘 학습을 위한 데이터셋(Data Set)을 구성하고, 데이터불균형 문제를 해결하기 위해 오버샘 플링(OverSampling) 기법 중 하나인 SMOTE(Synthetic Minority Oversampling Technique)을 적용하였다. 최종적으로 Random Forest Classification Model을 학습하고, 모델의 하이퍼파라미터 조정(HyperParameter Tunning)을 거처 결빙사 고 발생구간 예측성능이 가장 높은 모델을 결정하였다. 이를 통해, 전국 고속국도의 구간별 결빙사고 발생 위험도를 평 가하고 각 결빙사고 영향인자의 변수중요도를 분석함으로써 결빙 취약구간 평가 방안의 신뢰성 제고를 기대한다.

The purpose of this study was to identify and evaluate hazardous road sections based on roadside friction. Using GIS mapping and clustering techniques, this study analyzed traffic accidents and roadside friction data based on latitude and longitude coordinates. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied, with parameters of MinPts = 5 and eps = 0.0001, determined through a K-nearest neighbor analysis. The data were separated based on traffic flow direction (uphill/ downhill), and clustering was performed separately in each direction to identify specific hazard zones. The DBSCAN clustering results revealed 18 clusters in traffic accident data and 44 clusters in roadside friction data. Traffic accident clusters include various types of accidents (e.g., vehicle-to-vehicle and vehicle-to-pedestrian accidents), identifying locations as high-accident zones. The clustering results from the roadside friction data highlighted areas with crosswalks, absence of curbs, and roadside parking zones as major risk sections. Future research should analyze the operational design domain (ODD) of autonomous vehicles on hazardous road sections and explore the integration of multiple data sources to establish a comprehensive safety management system for accident prevention in autonomous driving environments. Additionally, road hazard sections are categorized into stages (e.g., hazardous, cautious, and safe) to enhance the precision in assessing road conditions. This categorization, combined with a detailed analysis of ODD, serves as a foundation for future research aimed at improving the safety of autonomous driving environments.

This study aims to calculate the passenger car equivalent (PCE) of heavy vehicles on one-lane exit sections of underground roads. Traffic-flow simulations were performed using the VISSIM software program. The scenarios were designed under varying conditions, including design speeds of 40 and 50 km/h, slope gradients ranging from 3% to 9%, and heavy vehicle proportions between 5% and 40%. The mixed-traffic flow capacity was calculated for each scenario at the threshold levels, and the PCE was estimated based on the two capacity conditions. The results revealed that the PCE decreased at lower design speeds. For slopes less than 6% at a design speed of 40 km/h, and for slopes less than 4% at 50 km/h, the PCE remained consistent across all conditions. Single-lane exit sections are often employed on underground roads because of congestion and structural constraints. However, when heavy vehicles are present in these sections, the overall traffic flow is dictated by the speeds of the heavy vehicles. To address this issue, the implementation of variable or temporary two-lane exit sections has been proposed to enhance traffic flow and capacity.

고속도로의 제한속도는 교통류, 운행 시간, 에너지 소비, 교통사고 발생률 등에 직접적인 영향을 미치는 중요한 요인이다. 제한속도 의 상향 조정은 운행 시간 단축과 경제적 이점을 가져올 수 있지만, 교통사고 위험성을 높일 수 있으며, 반대로 하향 조정은 사고율을 감소시킬 수 있으나 운행 시간 증가와 교통 혼잡을 초래할 수 있다. 이러한 상반된 영향으로 인해 제한속도 조정이 도로 안전성과 효 율성에 미치는 구체적인 변화를 분석하는 연구가 필요하다. 본 연구는 미시교통시뮬레이션 도구인 VISSIM과 SSAM을 활용하여 제한 속도 및 교통량 변화에 따른 고속도로 구간별 상충횟수를 분석하고, 위험성이 높아지는 구간을 식별하였다. 이를 통해 향후 단속지점 설정과 구간별 맞춤형 개선방안 마련에 실증적인 근거를 제공하고자 한다.

PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

PURPOSES : The type and degree of structural conditions and influencing factors distributed across representative sections should be similar to those distributed across entire sections as the representative sections have been predominantly used for developing performance prediction models, which substitute entire sections of road pavement. Therefore, a logic that selects the representative sections with similar distributions of structural conditions and the influencing factors with those of entire expressway asphalt pavement sections requires development. METHODS : The logic developed in this study to select the representative sections of asphalt pavements comprised three steps. First, the data on the structural conditions of the pavement and the influencing climate conditions and pavement materials were collected and organized. Consequently, in the second step, the candidate sections were selected, with the severity of the structural conditions of the pavement distributed widely and evenly. Finally, in addition to the widely and evenly distributed pavement conditions, the representative sections with climatic conditions and pavement materials were selected. RESULTS : A total of 6,352 ordinary asphalt pavement sections and 596 composite asphalt pavement sections were selected as entire expressway asphalt pavement sections and the data were collected and organized according to the logic developed in this study. Three times the representation sections were selected as candidate sections and, finally, 85 sections were selected as representative sections. The distribution of structural conditions and influencing climate conditions and pavement materials in the representative sections were similar to those in the entire sections. In addition, the representative sections were spread evenly across the country. CONCLUSIONS : The sections presenting similar distributions of structural conditions and the influencing factors of entire expressway asphalt pavement sections could be selected in this study. Using the representative sections selected in this study, a remodeling index model will be developed for predicting the asphalt pavement sections that require large-scale repair.

The numerical analysis of two-dimensional transient flow around the obstacle with rotated square cross sections was carried out. The obtained velocity distributions for each time step and each rotation angle were imaged to provide data for CNN(convolutional neural network). Both classification and regression neural networks were used for prediction of rotation angle. As results The classification method incorrectly predicted the rotation angle in only 2 of the 470 images. The regression method predicted the rotation angle errors within except 2 out of 470 images. From these facts, it could be concluded that both methods can be sufficiently applicable to the flow analysis.

PURPOSES : The purpose of this study is to analyze the impact of the level of the light-environment and the driver's visual ability on the change in the driver's perception of a forward curved section at night. The study also aims to identify factors that should be considered to ensure safety while entering curved sections of a road at night. METHODS : Data collected from a virtual driving experiment, conducted by the Korean Institute of Construction Technology (2017), were used. Logistic regression was applied to analyze the effects of changes in the light-environment factors (road surface luminance and glare) and the driver’s visual ability on a driver's perception of the road. Additionally, analysis of the moderated effect of visual ability on light-environment factors indicated that the difference in drivers’ visual abilities impact the influence of light-environment factors on their perception. A driver's ability to perceive, as a response variable, was categorized into 'failure' and 'success' by comparing the perceived distance and minimum reaction sight distance. Covariates were also defined. Road surface luminance levels were categorized into 'unlit road surface luminance' (luminance ≤ 0.1 nt) and 'lit road surface luminance' (luminance > 0.1 nt), based on 0.1 nt, which is the typical level observed on unlit roads. The glare level was categorized as 'with glare' and 'without glare' based on whether the glare was from a high-beam caused by an oncoming vehicle or not. The driver's visual ability level was categorized into 'low visual ability' (age ≥ 50) and 'high visual ability' (age ≤ 49), considering that after the age of 50, the drive’s visual ability sharply declines. RESULTS : The level of road surface luminance, glare, and driver's visual ability were analyzed to be significant factors that impact the driver's ability to perceive curved road sections at night. A driver's perception was found to reduce when the road surface luminance is very low, owing to the lack of road lighting ('unlit road luminance'), when glare is caused by oncoming vehicles ('with glare'), and if the driver's visual ability level is low owing to an older age ('low visual ability'). The driver's ability to perceive a curved section is most affected by the road surface luminance level. The effect is reduced in the order of glare occurrence and the driver's visual ability level. The visual ability was analyzed as a factor that impacts the intensity of the effect of change of the light-environment on the change of the driver's ability to perceive the road. The ability to perceive a curved section deteriorates significantly in 'low visual ability' drivers, aged 50 and above, compared to drivers with 'high visual ability,' under the age of 49, when the light-environment conditions are adverse with regard to the driver’s perception (road surface luminance: 'lit road surface luminance'→'unlit road surface luminance,' glare: 'without glare'→'with glare'). CONCLUSIONS : Supplementation, in terms of road lighting standards that can lead to improvements in the level of light-environment, should be considered first, rather than the implementation of restrictions on the right of movement, such as restricting the passage of low visual ability or aging drivers who are disadvantageous in terms of gaining good perception of the road at night. When establishing alternatives so that safety on roads at night is improved, it is necessary to consider improving drivers' perception by expanding road lighting installation. The road lighting criteria should be modified such that the glare caused by oncoming traffic, which is an influential factor in the linear change in perception, and the level of light-environment thereof are improved.

PURPOSES : The objective of this study is to figure out the trend and characteristics of fine particulate matter (PM2.5) and nitrogen oxide (NOx) concentration in underpass sections. The effect of traffic and meteorological condition on PM2.5 / NOx concentration was analyzed using field monitoring data. METHODS : Based on the literature review, PM2.5 and NOx concentration data were monitored using DustTrak II aerosol monitoring system and Serinus 40 oxides of nitrogen analyzer, respectively. Meteorological and traffic information was collected using automatic weather system and traffic volume counter, respectively. RESULTS : PM2.5 has a positive and negative correlation with relative humidity and wind speed, respectively. Meanwhile, NOx was found to have no correlation with meteorological conditions. The NO/NO2 ratio tends to change with traffic volume, indicating higher correlation between NO and traffic volume; the observed NO2 is mostly a secondary material produced by NO oxidation. CONCLUSIONS : Our study provides clear characteristics of NOx and PM2.5 and correlations with meteorological and traffic information in the underpass sections. It is found from this study that the increase in wind speed causes reduction in the concentration of PM2.5 owing to the diffusion and dispersion phenomena. On the other hand, the meteorological conditions were found to barely have correlations with NOx concentrations in this study. The traffic volume could significantly affect the NOx concentration and NO / NO2 ratio, which is directly correlated to the emissions from vehicles.

국내 하천에서 발생하는 준설 및 보 건설은 하천 연속성 차단과 교란을 유도하여 수서 생물서식환경에 변화를 가져 온다. 본 연구에서는 4대강 보 (이포보, IP; 세종보, SJ; 죽산보, JS; 강정고령보, GG; 달성보, DS)에 서식하는 깔따구 군집 분포를 조사하고 서식환경에 영향을 주는 여러 환경인자를 측정하였다. 조사 지역 중 IP, SJ은 다른 조사 지역에 비해 WT, pH, TOC, Chl-a가 낮은 수준을 보였으며, 깔따구 개체수 결과에서는 Chironominae, Orthocladinae, Tanypodinae 가 비교적 균등한 수준으로 관찰되었다. 반면, JS, GG, DS는 Chironominae가 높은 비율로 우점하며, TOC와 Chl-a의 농도가 높게 나타났다. 각 조사 정점에 대한 깔따구 군집 조성의 특징과 환경요인을 반영한 집괴분석 결과 4대강 보는 3개의 그룹으로 구분되었으며, 이는 정점별 환경 차이와 깔따구의 대악 및 하순기절의 구조에 따른 먹이원 선호도 차이와 일치하였다. 따라서 본 연구에서는 연구 정점 간 먹이원의 차이에 의해 깔따구의 군집 구조의 차이가 나타나는 것을 확인 하였으며, 향후 각 깔따구 과 별 주 먹이원에 대한 연구에 대한 필요성을 제시한다.

본 논문에서는 AISC 표준 단면을 설계 변수로 하는 캔틸레버 타입 헬리데크 모델의 유전 알고리즘 최적설계를 소개한다. AISC 표준 단면을 단면 형상별로 분류하고 단면적 순으로 정렬한 후 정수 단면 번호를 부여하여 설계 변수로 최적설계를 수행하였다. 이 과정을 통하여 이산화된 설계 변수를 가지는 최적설계 문제를 해결하기 위해 유전 알고리즘을 적용하였다. 또한, 제약조건으로 허용응력 및 허용응력비 검사 조건을 모두 고려하여 구조물의 구조 안정성을 고려한 설계를 수행하였다. 최적설계 과정중 매 반복계산 마다 수행되는 구조해석 시간을 단축시키기 위해 선형 중첩법을 사용하였고, 이를 통해 구조 해석 시간을 약 75% 감소시킬 수 있었다. 또한 헬리데크 최적설계의 경량 효과를 높이기 위해 부재 그룹 세분화를 하였고, 그 결과를 선행 연구 모델, 기존의 부재 그룹 모델과 비교하였다. 그 결과 선행연구 대비 약 30톤의 부재를 절감할 수 있었으며, 구조적으로도 보다 안전한 헬리데크 설계를 얻을 수 있었다.

PURPOSES: The objective of this study is to evaluate the structural capacity of asphalt pavement in subsurface cavity sections using falling weight deflectometer (FWD) backcalculation method. METHODS: It is necessary to analyze the reduction of structural capacity in asphalt pavements due to the occurrence of subsurface cavities. The FWD testing was conducted on the cavity and intact asphalt pavement in the city of Seoul. The GAPAVE, backcalculation program for FWD deflections, was utilized to determine the layer moduli in asphalt pavements. The remaining life of asphalt pavements in cavity sections were predicted using the pavement performance model for fatigue cracking. The backcalculated layer moduli between cavity and intact sections were compared to determine the reduction of structural capacity due to subsurface cavity. The relationship between the reduction of layer modulus and cavity depth/length was analyzed to estimate the effect of cavity characteristics on the structural capacity degradation. RESULTS: According to the FWD backcalculation results, the modulus of asphalt layer, subbase, and subgrade in cavity sections are generally lower than those in intact sections. In the case of asphalt layers, the backcalculated modulus in cavity section was reduced by 50% compared to intact section. A study for the prediction of remaining life of cavity section shows that the occurrence of subsurface cavity induces the decrease of the pavement life significantly. It is found that there is no close relationship between the backcalculated modulus and cavity length. However, the reduction of asphalt layer modulus is highly correlated with the cavity depth and was found to increase with the decrease of cavity depth. CONCLUSIONS : This reduction of structural capacity due to the occurrence of cavities underneath asphalt pavements was determined using FWD backcalculation analysis. In the future, this approach will be utilized to establish the criteria of road collapse risk and predict the remaining life of cavity sections under numerous varied conditions.