2019년 12월, 상주-영천 고속도로 상행선에서 도로 노면 결빙에 의한 연쇄추돌사고로 48명의 사상자가 발생하였다. 이에, 국토교통부 는 2020년 1월 결빙 취약구간 선정기준을 마련하여 결빙 취약구간 403개소를 지정하고, 결빙 취약구간을 대상으로 2022년까지 1,699억 원의 예산을 투입하여 결빙사고 예방사업을 계획하였다(BAI, 2021). 하지만, 결빙 취약구간 선정기준에 대해 적정성 검토가 이루어지 지 않아 그 신뢰성과 실효성이 충분히 검증되지 않았다. 본 연구에서는 국가교통정보센터의 노드·링크(Node·Link) 체계를 기반으로 전국 고속국도 및 일반국도의 특성정보(시설, 선형구조, 기상, 교통 등)를 GIS(Geographic Information System) 데이터로 구축하였다. 최근 5년 결빙사고 발생이력이 있는 도로구간(Link)을 확인하고 Random Forest 알고리즘을 통해 도로 특성정보의 결빙사고에 대한 변수 중요도(Feature Importance)를 분석했다. 이를 통해 결빙사고와 각 인자의 상관성을 파악하여 ‘결빙 취약구간 평가 세부 배점표’의 항목별 배점을 수정, 보완함으로써 평가표의 신뢰성을 제고한다.

Black ice, a thin and nearly invisible ice layer on roads and pavements, poses a significant danger to drivers and pedestrians during winter due to its transparency. We propose an efficient black ice detection system and technique utilizing Global Positioning System (GPS)-reflected signals. This system consists of a GPS antenna and receiver configured to measure the power of GPS L1 band signal strength. The GPS receiver system was designed to measure the signal power of the Right-Handed Circular Polarization (RHCP) and Left-Handed Circular Polarization (LHCP) from direct and reflected signals using two GPS antennas. Field experiments for GPS LHCP and RHCP reflection measurements were conducted at two distinct sites. We present a Normalized Polarized Reflection Index (NPRI) as a methodological approach for determining the presence of black ice on road surfaces. The field experiments at both sites successfully detected black ice on asphalt roads, indicated by NPRI values greater than 0.1 for elevation angles between 45o and 55o. Our findings demonstrate the potential of the proposed GPS-based system as a cost-effective and scalable solution for large-scale black ice detection, significantly enhancing road safety in cold climates. The scientific significance of this study lies in its novel application of GPS reflection signals for environmental monitoring, offering a new approach that can be integrated into existing GPS infrastructure to detect widespread black ice in real-time.

최근 국내 겨울철 블랙아이스(Black Ice)로 인해 발생하는 교통사고가 증가하는 추세이며, 한국 도로교통공단 조사 결 과 2016～2020년 겨울철까지 블랙아이스로 인한 사고는 총 4,868건이며, 사상자는 8,938명인 것으로 조사 되었다. 도로상 태에 따라 건조대비 동결상태에서 교통사고 발생시 치사율이 43%로 높게 나타났다. 이러한 사고는 기온이 떨어지는 12 월부터 급증하여, 최저기온이 가장낮은 1월까지 증가한다. 블랙아이스는 도로에 쌓인 눈이 융해(해설)과 동시에 도로 위 각종 이물질과 결합 후 재동결하여 흑색 동결막을 형성하는 것을 말한다. 그 특성상 운전자가 차량내부에서 도로의 상태 를 쉽게 파악할 수 없으며 대부분의 운전자가 차량이 미끄러지기 시작함과 동시에 인지하여 사고가 발생하게 된다. 이에 본 연구에서는 기존 포장체의 미끄럼 저항도를 상태별로 비교 분석하였다. 포장체의 미끄럼 저항성 정도를 파악하기 위 해 영국식 미끄럼저항 시험기 (British Pendulum Tester ; BPT)를 사용하였으며, 포장체의 종류로는 일반적인 밀입도 아스팔트 포장, 배수성 아스팔트 포장, 그루빙(포장 표면에 일정한 규격의 홈을 형성)을 적용한 콘크리트 포장, 그루빙이 없는 콘크리트 포장을 적용하였다. 미끄럼저항 실험은 관련 KS규격 및 ASTM규격에 준하여 실시하되 블랙아이스를 모 사하기위하여 표면온도 영하 2～3℃ 샘플에 강우를 모사한 물을 분사하며 영하 9℃로 10분 동결 후 2mm강수량을 모사 한 수분을 재 분사한 후 시험을 실시하였다.

PURPOSES : The purpose of this study was to develop techniques for forecasting black ice using historical pavement temperature data collected by patrol cars and concurrent atmospheric data provided by the Korea Meteorological Administration. METHODS : To generate baseline data, the physical principle that ice forms when the pavement temperature is negative and lower than the dew-point temperature was exploited. To forecast frost-induced black ice, deep-learning algorithms were created using air, pavement, and dew point temperatures, as well as humidity, wind speed, and the z-value of the historical pavement temperature of the target segment. RESULTS : The suggested forecasting models were evaluated against baseline data generated by the above-mentioned physical principle using pavement temperature and atmospheric data gathered on a national highway in the vicinity of Young-dong in the Chungcheongbukdo province. The accuracies of the forecasting models for the bridge and roadway segments were 94% and 90%, respectively, indicating satisfactory results. CONCLUSIONS : Preventive anti-icing maintenance activities, such as applying anti-icing chemicals or activating road heating systems before roadways are covered with ice (frost), could be possible with the suggested methodologies. As a result, traffic safety on winter roads, especially at night, could be enhanced.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.

of actual and suspicious black-ice cases that occurred during the last 10 years in the Republic of Korea. METHODS : Based on literature review, meteorological observation data associated with black-ice formation are selected: wind speed, air temperature (T), dew point temperature (Td), and relative humidity, to set minimum or maximum threshold values based on the normal distribution of each variable. In addition, weights are assigned based on the relationship among the variables to calculate the probability of occurrence. RESULTS : The threshold values are calculated using the average and standard deviation, resulting in 7.65 °C, 56.63%, 2.99 ms-1 for T-Td, relative humidity, and wind speed, respectively. Whereas the threshold value of T-Td and wind speed is set to the maximum threshold, that of the relative humidity is set to the minimum threshold value. These threshold values are applied to the diagnosis algorithm of black-ice formation, including a 1-h accumulated precipitation. CONCLUSIONS : The algorithm is expected to be utilized as a research methodology for diagnosing suspected cases of black ice.

PURPOSES : This study aims to determine the type (e.g., melting point, freezing point, latent heat fusion) and optimal content of phase change material (PCM) based on the numerical and experimental analyses evaluating the effects of heat transfer in PCM-modified asphalt pavement systems. METHODS : The effect of PCM on the thermophysical properties of PCM-modified asphalt concrete can be taken as an effective volumetric heat capacity. The volumetric fraction of PCM was calculated using an iterative method. The numerical model was established and computed using the MATLAB 2020 software. The optimum PCM design tool was developed to select the type and contents of the PCM. The PCM was chosen based on the following criteria: black-ice-formation delay time, minimize temperature increase, and increase temperature area. To validate the numerical model, asphalt mixtures were modified with varying PCM contents, and the temperature response of the PCMmodified asphalt samples was examined via temperature test. RESULTS : The numerical results showed that incorporating PCM into the asphalt mixture can slow the cooling rate of the pavement system. The predicted results from the optimum PCM design tool were highly consistent with the measured values from the laboratory temperature test. CONCLUSIONS : The temperature of PCM-modified asphalt pavement can be predicted via numerical method. The effect of PCM on the thermophysical properties can be considered as effective volumetric heat capacity; while the volume fraction of PCM can be calculated via an iterative method. The accuracy of the numerical model was confirmed by a high agreement between the measured and predicted values.

Freezing rain is a phenomenon when precipitation falls as a liquid rain drop, but freezes when it comes into contact with surfaces or objects. In this study, we investigated the predictability of freezing rain and its characteristics, which are strongly related with the occurrence of black ice using synoptic scale meteorological observation data. Two different cases occurred at 2012 were analyzed and in the presented cases, freezing rain often occurs in the low-level low pressure with the warm front. The warm front due to the lower cyclone make suitable environment in which snow falling from the upper layer can change into supercooled water. The 0℃ temperature line to generate supercooling water is located at an altitude of 850 hPa in the vertical temperature distribution. And the ground temperature remained below zero, as is commonly known as a condition for black ice formation. It is confirmed that the formation rate of freezing rain is higher when the thickness after 1000-850 hPa is 1290-1310 m and the thickness of 850-700 hPa layer is larger than 1540 m in both cases. It can also be used to predict and estimate the generation of freezing rain by detecting and analyzing bright bands in radar observation.