• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.165-173
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• 2008

This study aims at generating automatically three dimensional geometric models of roads using LIDAR data and a digital map. The main processes in the proposed method are (1) generating a polygon encompassing a road region using a road layer from the digital map, (2) extracting LIDAR points within the road region using the polygon, (3) organizing the points into surface patches and grouping the patches into surface clusters, (4) searching the road surface clusters and generating the surface model from the points linked to the clusters, (5) refining the boundary using a digital map. By applying the proposed method to real data, we successfully generated the linear and surface information of the roads.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.123-131
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• 2011

This study proposes a model for road surface temperature prediction on basis of the heat-energy balance equation between atmosphere and road surface. The overall model is consisted of two types of modules: 1) Canopy 1 is used to describe heat transfer between soil surface and atmosphere; and 2) Canopy 2 can reflect the characteristics of pavement type. Input data used in the model run is obtained from the Korea Meteorological For model validation, the observed and predicted surface temperature data are compared using data collected on MoonEui Bridge along CheongWon-Sangju Expressway, and the comparison is made on winter and other seasons separately. Analysis results show that average difference between two temperatures lies within ${\pm}2^{\circ}C$ which is considered as appropriate from a micrometeorology point of view. The model proposed in this paper can be adopted as a useful tool in practical applications for winter maintenance. This study being a fundamental research is anticipated to be a starting point for further development of robust surface road temperature prediction algorithms.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.391-392
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• 2010

The frozen road is effected as major cause of car accident in winter. Especially, road surface freezing on the highway can lead to fatal accident. The accident by frozen road can effectively reduced by prevent road surface freezing before it frozen as evaluate road surface condition. Therefore, this study installed thermometer in each depth of concrete pavement for evaluate road surface conditions which freezing chronically. The result of this study will be used as preliminary data for predict before freezing.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1507-1516
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• 2020

This study analyzed the scenario for road freezing section by applying the road freezing evaluation algorithm. To apply road freezing algorithm, the influencing factors on road freezing were reviewed. Observation data from four points, Mokgam IC, Jeongneung tunnel, Seongsan bridge, and Yeomchang bridge were used for analysis. All observatories are installed on the expressway, and they are classified for the analysis of road freezing characteristics. When the difference between the road surface temperature and dew-point temperature of the road freezing evaluation algorithm was 3℃ or less, the section where road freezing occurred was checked. In addition, road freezing evaluation was derived through the change of the road surface condition and water film thickness of the freezing section.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.92-98
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• 2010

Virtual ground modeling is one of key topic for real-time vehicle dynamic simulation. This paper discusses about the virtual 3D road modeling process using parametric surface concept. General road data is a type of lumped position vector so interpolation process is required to compute contact of internal surface. The parametric surface has continuity and linearity within boundaries and functions are very simple to find out contact point. In this paper, the parametric surface formula is adopted to road modeling to calculate road hight. Position indexing method is proposed to reduce memory size and resource possession, and a simple mathematical method for contact patch searching is also proposed. The developed road process program is tested in dynamic driving simulation on off-road. Conclusively, the new virtual road program shows high performance of road hight computation in vast field of off-road simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1098-1104
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• 2011

This paper presents the development of the surface profile sensing system (SPSS) and its application to analysis of road surface. The SPSS which can robustly reconstruct the road input profiles from the intermixed data with the vehicle's dynamic motion, is implemented using the multi-sensor system with the optimally shaped transfer function. The performance of this system is evaluated by a series of experimental works in the devised simulator. And a real car test equipped with the proposed system is performed in the proving ground over both deterministic and random road surfaces. Finally, a feasibility of the system is investigated considering the road model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.2
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• pp.30-38
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• 2015

In this study, to improve effectiveness of road management services and the safety of the road in winter, road surface temperature prediction model was developed. We have utilized the existing input data of meteorological data and additional traffic data. This Road surface temperature prediction model was utilizing a Heat-Balance Method additionally considering amount of traffic that produce heat radiation by vehicle-tire friction. This improved model was compared to the based model to check into influence of traffic affecting the road surface temperature. There were verified by comparing the real observed road surface temperature of the third Gyeong-In highway and road surface temperature from the two models. As a result, the error of real observed and the predicted value (RMSE) was found to average $1.97^{\circ}C$. Observed road surface temperature was dramatically affected by the sunlight from 6 a.m. to 2 p.m. and degree of influence decreases after that. The predictive value of the model is lower than the observed value in the afternoon, and higher at night. These results appear due to the shielding of solar radiation caused by the vehicle in the afternoon and at night, the vehicle appeared to cause thermal heat supply.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.211-218
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• 2012

Two experiment are planed to identify driver's safe driving behaviour by curve radius, road surface condition in curve section. At four-lane and two-lane road, conducted experiments are check on driver's feeling of safety that 30 subjects do not feel discomfort. And using the data from these experiments, this study compare physical speed (not slipping, fall our of the road) with safety driving speed(drivers felt a comfortable and safe speed) each curve radius and fiver road surface condition(drying, wet, rain, snow and ice). As a result, safe driving behaviour factors that are derived to curve radius of 100m units, five road surface conditions enable to represent quantitative analysis of driver's discomfort. This study will develop road design method and evaluation reflected ergonomic aspects.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.