• 대기
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• 제24권4호
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• 한국도로학회논문집
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• 제15권5호
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• pp.177-185
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• 2013

PURPOSES : This study attempts to design and establish the road surface condition detection system by using the image processing that is expected to help implement the low-cost and high-efficiency road information detection system by examining technology trends in the field of road surface condition information detection and related case studies. METHODS : Adapted visual information collecting method(setting a stereo camera outside of the vehicle) and visual information algorithm(transform a Wavelet Transform, using the K-means clustering) Experiments and Analysis on Real-road, just as four states(Dry, Wet, Snow, Ice). RESULTS : Test results showed that detection rate of 95% or more was found under the wet road surface, and the detection rate of 85% or more in snowy road surface. However, the low detection rate of 30% was found under the icy road surface. CONCLUSIONS : As a method to improve the detection rate of the mobile road surface condition information detection system developed in this study, more accurate phase analysis in the image processing process was needed. If periodic synchronization through automatic settings of the camera according to weather or ambient light was not made at the time of image acquisition, a significant change in the values of polarization coefficients occurs.

• 한국안전학회지
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• 제32권6호
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• pp.150-156
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• 2017

A frozen road surface increases traffic accidents during the winter season. Hence, information on easily-frozen road sections and their specificities are required to prevent traffic accidents. Frozen road surfaces are determined by equipment measuring road surface temperatures. However, there are limitations in investigating the entire road network. Therefore, it is imperative to develop new methods that effectively determine road surface freezing risks. Meteorologically, road surfaces are frozen when the actual temperature cools down to the dew point temperature. Under this condition, there is likely to be frost if relative humidity reaches 100% and frozen road surfaces as the temperature gets lower. Meteorological characteristics give us an alternative to a direct measurement road surface temperature to estimate risks of road surface freezing. Based on the clues, the relationship between severity of traffic accidents and temperature changes is empirically investigated using Paju weather data. The results reveal that as the temperature gets lower and changes in current temperature are relatively small, the severity of traffic accidents become higher. In addition, the same is true when the difference between current temperature and the dew point temperature is relatively small, as it increases possibilities of road surface freezing. Future studies must investigate how current temperature and the dew point temperature affect road surface freezing and thereby establish a time-space scope to estimate possible road surface freezing sections using only weather and road material type data. This would provide invaluable information for predicting and preventing frozen road accidents based on weather patterns.

• Tribology and Lubricants
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• 제33권6호
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• pp.303-308
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• 2017

Most automobile tire companies have not yet considered the geometric information of a road at the design stage of a tire because the topographical characterization of a road surface is very difficult owing to its vastness and randomness. A road surface shows variable surface roughness values according to magnification, and thus, the contact force between the road and tire significantly fluctuates with respect to the scale. In this study, we make an attempt to define a representative value for surface topographical information at multi-scale levels. To represent surface topography, we use a statistical method called power spectral density (PSD). We use the fast Fourier transform (FFT) and PSD to analyze the height profiles of a random surface. The FFT and PSD of a surface help in obtaining a fractal dimension, which is a representative value of surface topography at all length scales. We develop three surfaces with different fractal dimensions. We use finite element analysis (FEA) to observe the contact forces between a tire and the road surfaces with three different fractal dimensions. The results from FEA reveal that an increase in the fractal dimension decreases the contact length between the tire and road surfaces. On the contrary, the average contact force increases. This result indicates that designing and manufacturing a tire considering the fractal dimension of a road makes safe driving possible, owing to the improvement in service life and braking performance of the tire.

• 대한임베디드공학회논문지
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• 제17권3호
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• pp.149-155
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• 2022

This paper proposes a method for estimating the road surface condition and tilt angle using an inertial measurement unit (IMU) to improve the safety in the use of mobility aids for the elderly. The measurements of the accelerometers of the IMU usually include the accelerations caused by not only the gravitational force but also linear and rotational motions. Thus, the gravitational accelerations are first extracted using several physical constraints and then incorporated into the Kalman filter to estimate the tilt angle. In addition, because the magnitudes of the accelerations produced by the rotational motions (roll and pitch motions) vary with the road surface condition, a criterion based on such accelerations is presented to classify the condition of the road surface. The obtained road surface condition and tilt angle are finally combined to provide the safety information (e.g., safe, warning, and danger) for the user to improve the walking safety. Experiments were carried out and the results showed that the proposed method can provide the condition of the road surface, the tilt of the road surface, and the safety information correctly.

• 한국환경과학회지
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• 제31권4호
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• pp.329-339
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• 2022

Better understanding the mechanism of black ice occurrence on the road in winter is necessary to reduce the socio-economic damage it causes. In this study, intensive observations of road weather elements and surface information under the influence of synoptic high-pressure patterns (22^nd December, 2020 and 29^th January, and 25^th February, 2021) were carried out using a mobile observation vehicle. We found that temperature and road surface temperature change is significantly influenced by observation time, altitude and structure of the road, surrounding terrain, and traffic volume, especially in tunnels and bridges. In addition, even if the spatial distribution of temperature and road surface temperature for the entire observation route is similar, there is a difference between air and road surface temperatures due to the influence of current weather conditions. The observed road temperature, air temperature and air pressure in Nongong Bridge were significantly different to other fixed road weather observation points.

• 한국인공지능학회지
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• 제11권4호
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• pp.21-27
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• 2023

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

• 한국도로학회논문집
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• 제20권2호
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• 한국ITS학회 논문지
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• 제17권6호
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• pp.121-132
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• 2018

기상은 교통흐름, 운전자의 주행패턴, 교통사고 등 여러 방면에서 도로교통에 영향을 미치는 중요한 요인이다. 본 연구는 기상상황과 노면상태 사이의 관계에 초점을 맞추어 기계학습을 통해 도로의 노면상태를 추정하는 모델을 개발하였다. 노면 상태의 수집을 위해 실험 차량에 노면센서를 부착하여 '건조', '습윤', '젖음', 3가지 범주로 구분된 노면상태 정보를 수집하였고, 이를 추정하기 위한 변수로 도로의 기하구조 정보(곡률, 구배), 교통정보(교통량), 기상정보(강우량, 습도, 온도, 풍속)를 활용하였다. 노면 상태를 예측하기 위한 알고리즘으로는 다양한 기계학습 알고리즘이 검토되었으며, 그 중 가장 높은 정확도를 보인 'Random forest'를 기반으로 한 2단계 분류모형을 구축하였다. 총 16일의 실측 데이터 중 14일의 데이터를 모델을 학습하는 데 활용하였고, 2일의 데이터를 모형의 정확도를 검증하기 위해 사용하였다. 그 결과 81.74%의 검증 정확도를 가지는 노면상태 예측 모델을 구축하였다. 본 연구의 결과는 기상청에서 관측하는 기상정보로 도로의 노면상태를 추정할 수 있다는 가능성을 보여주며, 새로운 장비나 센서를 설치하지 않고도 기존의 기상 관측 정보와 교통정보 등을 활용하여 노면의 상태를 추정할 수 있음을 시사한다.

• 한국측량학회지
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• 제23권1호
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• pp.9-17
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• 2005

본 연구는 디지털 비디오카메라를 이용하여 아스팔트 콘크리트 포장의 노면을 촬영하고 획득된 영상을 분석함으로써, 대상 도로노면의 정보를 추출하는데 그 목적이 있다. 먼저, 수치영상처리에 의한 도로노면정보의 정확도를 분석하기 위해 기준점 측량에 의한 성과와 비교·분석하였다. 그 결과, X방향으로 0.0427m, Y방향으로 0.0527m, Z방향으로 0.1539m의 평균오차를 나타내었으며, 이는 축척 1/1,000 이하의 지도제작 및 GIS 자료로 충분히 활용성이 있는 것으로 판단된다. 또한, 처리된 수치영상을 분석하여 도로노면의 평가를 위한 중요 요소인 균열률, 소성변형량, 그리고 종단평탄성 정보를 획득할 수가 있었으며, 이를 이용하여 공용성지수와 유지관리지수를 산출함으로써, 대상도로의 노면평가를 수행할 수가 있었다. 향후 도로를 대상으로 취득된 양질의 영상정보를 축적함으로 인해 포장유지관리시스템 분야에 있어서 중요한 기초 자료를 제공할 수 있을 것으로 기대된다.