• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.69-72
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• 2000

This study proposes a new calculation method for generating real nighttime lamp-lit images. In order to improve the color appearance in the prediction of a nighttime lamp-lighted scene, the lamp-lit image is synthesized based on spectral distribution using the estimated local spectral distribution of the headlamps and the surface reflectance of every object. The principal component analysis method is introduced to estimate the surface color of an object, and the local spectral distribution of the headlamps is calculated based on the illuminance data and spectral distribution of the illuminating headlamps. HID and halogen lamps are utilized to create beam patterns and captured road scenes are used as background images to simulate actual headlamp-lit images on a monitor. As a result, the reproduced images presented a color appearance that was very close to a real nighttime road image illuminated by single and multiple headlamps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.154-160
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• 1995

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading. To automatically extract a road map directly form more complicated topographical maps, a very complicated algorithm is needed, simce the image generally involves such complicated patterns as symbols, characters, residential sections, rivers,etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Journal of the Korean Institute of Rural Architecture
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• v.9 no.2
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• pp.41-48
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• 2007

This paper aims to clarify the Spatial Form of the Port Settlement of Rivershore in Nakdong-river. The web of road is composed of three basic elements, kyeongjeon-seon railroad, inner old coastal street, a narrow gauge railway. The basic road system is composed of Hon-machi dori (Main street) parallel to the coastal line and three perpendicular roads. According to these road patterns, plot of the lands in the block are formed into distinctive trapezoidal shape. To conclude, the Spatial Form of Samrangjin's Port Settlement might explain as a relation of Rivershore's shape and marketplace's spatial form.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1496-1505
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• 1995

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading. To automatically extract a road map dircetly from complicated topographical maps, a very sophisticated algorithm is needed, since the image generally involvfes such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper proposes a new feature extraction method based on the morphology. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Journal of Environmental Impact Assessment
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• v.14 no.6
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• pp.427-441
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• 2005

In order to choose proper dispersion model and emission factors suitable in Korea in evaluating the effect of pollutants emitted by the vehicles in highway on nearby area, various road dispersion models and vehicle emission factors were reviewed. With theoretical inter-comparisons of the exiting models for line source, CALINE 3 and CALINE 4 models which were suggested by US EPA were selected as the road dispersion models for further evaluation with the measurement. The emission factors suggested by Korean Ministry of Environment was turned out to be appropriate since the classification of vehicle kinds was simple and easy to apply in Korea. The comparisons of predicted concentrations by CALINE 3 and 4 models with the measurements in flat, fill and bridge road types showed that CO and PM-10 were in good agreements with experiments and the differences between CALINE 3 and 4 models are negligible. The model concentrations of $NO_2$ by CALINE 4 were also in good agreement with the measurement but those by CALINE 3 were over-predicted. The discrepancies in CALINE 3 model were due to rapid decay reaction of $NO_2$ near the highway, which was not included in CALINE 3 model. For the road type with one & two side cutting grounds, the similar patterns as the flat & fill road type for CO, PM10, & $NO_2$ were observed but the number of data for comparison in these cases were not enough to draw the conclusion. These results lead to the conclusion that CALINE4 model is proper in road environmental impact assessment near the highway in flat, fill and bridge road types.

• The Journal of the Acoustical Society of Korea
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• v.12 no.3
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• pp.43-48
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• 1993

This study was carried out to verificate the possibility of using the laboratory setting psycho-acoustic experiment compared with the field and to delineate the appropriate indices in evaluating the Noisiness of road traffic noise by means of psycho-acoustic method. Reviewing the typical patterns of traffic noises depending upon the shapes ad conditions of the road, the road traffic noises in several representative points in the major residential areas in Kwangju city were recorded and reproduced with the noise levels modified in various steps. With these 20 sound sources, psycho-acoustic experiments in the laboratory were performed on 11 volunteer subjects. And then, psycho-acoustic experiments in the real field were performed on 10 volunteer subjects to compare the results of the laboratory experiment, the results are summerized as follows : 1. The psycho-acoustic experiments in the laboratory elicited the data well matching with those obtained in the field, resulting in even higher corelation levels. This indicates that the field assessement of responses to the noise can be replaced by the evaluatioin in the laboratory settings which render many variables easily controlled and that the responses of the residents to the noise can be easily predicted in the laboratory by applying this method. 2. Also among the complex indices, such as Noise Pollution Level or Annoyance Index high correlations were detected. On the other hand, low corelations were noted among Traffic Noise Index. 3. Highly significant correlations were found among the direct indices such as Leq, L\sub 10\, On the other hand, low correlations were detected among L\sub 50\.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.452-460
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• 1994

Maps are one of the most complicated types of drawings. Drawing recognition technology is not yet sophisticated enough for automated map reading To automatically extract a road map directly from more complicated topographical maps, a very complicated algorithm is needed, since the image generally involves such complicated patterns as symbols, characters, residential sections, rivers, railroads, etc. This paper describes a new feature extraction method based on the human optical neural field. We apply this method to extract complete set of road segments from topographical maps. The proposed method successfully extract road segments from various areas.

• Journal of Korean Society on Water Environment
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• v.20 no.5
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• pp.494-499
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• 2004

Growth in population and urbanization has progressively increased the loadings of pollutants from non-point sources as well as point sources. Therefore, it is necessary to manage both point and non-point sources contaminations for protecting water environment and improving water quality. This study investigated the characteristics of pollutant release over a wide range of rainfall intensities as a requisite to control road runoff that accounts for the largest portion of non-point source contamination in urban areas. Samples of runoff rainwater collected from real road surfaces were analyzed for physicochemical parameters such as pH, suspended solids, and heavy metals. A experimental model road ($30cm{\times}30cm$) was also used to evaluate wash-off properties of pollutants deposited on the surface as functions of time and rainfall intensity. Analysis of runoff samples on rain events showed that the pollutant wash-off patterns for heavy metal and suspended solids were similar. This implies that the particles in rainwater adsorb heavy metals. Experiments using the model road made of impervious asphalt demonstrate a strong first flush phenomenon. At high rainfall intensity, approximately 80% of total pollutants were released within 15 min. The pollutant wash-off rates rapidly increase from 9 mm/hr to 12 mm/hr of rainfall intensity and decrease over 12 mm/hr of rainfall intensity.

• 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.