• 한국도로학회지:도로
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• v.2 no.4 s.6
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• pp.50-56
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• 2000

• 한국도로학회지:도로
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• v.17 no.1
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• pp.21-27
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• 2015

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.125-136
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• 2005

The current traffic accident reduction procedure in economic feasibility study does not consider the characteristics of road and V/C ratio. For solving this problem, this paper suggests methods to be able to evaluate safety of each road in construction and improvement through developing accident Prediction model in reflecting V/C ratio Per road types and traffic characters. In this paper as primary process, model is made by tke object of urban roads. Most of all, factor effecting on accident relying on road types is selected. At this point, selecting criteria chooses data obtained from road planning procedure, traffic volume, existence or non-existence of median barrier, and the number of crossing point, of connecting road. and of traffic signals. As a result of analyzing between each factor and accident. all appear to have relatives at a significant level of statistics. In this research, models are classified as 4-categorized classes according to roads and V/C ratio and each of models draws accident predicting model through Poisson regression along with verifying real situation data. The results of verifying models come out relatively satisfactory estimation against real traffic data. In this paper, traffic accident prediction is possible caused by road's physical characters by developing accident predicting model per road types resulted in V/C ratio and this result is inferred to be used on predicting accident cost when road construction and improvement are performed. Because data using this paper are limited in only province of Jeollabuk-Do, this paper has a limitation of revealing standards of all regions (nation).

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2575-2586
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• 2000

In this paper, we propose an algorithm which can automatically extract the road information in a map image. The proposed method extracts the road image in the complex map image. The extracted image is converted into the skeleton image by thining method. The converted image contains various problems. In order to correct these problems, after the road is classified by the number of Rutovitz-connectivity which represents the characteristic of road, those are respectively corrected according to the load characteristic. In the simulation, the proposed method has obtained good results for the various type of map images.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.294-299
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• 2020

Based on the combined results of field surveys and analyses of the road structure and traffic flow, we propose a new plan for reducing roadside non-point pollution in the Paldang Water Source Protection Zone. The results show that the soil surrounding the roads in Paldang is highly permeable, which mitigates the need for filtration facilities. Roads flanked by steep slopes are found to facilitate the reduction of non-point roadside pollution through vegetation and soils along road slopes without the need for pollution reduction facilities. These results highlight the need for a flexible roadside non-point pollution reduction plan for roadside non-point pollution, which can be tailored to compliment relevant regulations and design standards after analyzing the characteristics of the target road.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.375-382
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• 2010

This paper presents the extraction of natural frequencies and mode shapes of a cable-stayed bridge using data acquired from GNSS. The response signals of 6 GNSS measuring points installed at the Seohae cable-stayed bridge are used for analysis of dynamic characteristics. Using normalization process and a third order Butterworth filter for the measured signals, the related pass band's signals have been isolated. Then, the acceleration data by double differentiation for these signals are obtained. Total five natural frequencies have been extracted by the fast Fourier transform and compared to the results of different studies. For the acceleration obtained from GNSS signals, the mode shapes of the bridge have been successfully extracted by TDD technique.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• Geotechnical Engineering
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• v.38 no.4
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• pp.37-53
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• 2022

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.87-93
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• 2004

A five-day work week has a great impact on the life styles of employed persons and their families. At the same time, the changes also impact on the transportation system because travel patterns, demand, and pattern of congestion change during weekends. The negative impacts on the transportation system should be examined in order to conceive measures to maintain dependable levels of service during weekends. The first step to pursue the issue is to identify the road segments heavily affected by augmented leisure trips. In this study, characteristics of highway segments are engineered by principal analysis using data from TCS database. Scores from principal analysis are employed to distinguish highway segments for leisure trips from total 197 segments considered in this study. In addition, indexes from principal analysis are proposed to identify highway segments for leisure trips.