• 한국도로학회지:도로
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• v.6 no.2 s.20
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• pp.40-46
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• 2004

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.256-261
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• 2020

Roadside non-point pollution reduction facilities are classified as infiltration, vegetation, reservoir, and wetland types based on their respective pollution reduction mechanisms. However, without a detailed analysis of the road and traffic conditions it is very difficult for civil engineers to determine which category of pollution reduction facility is best suited to their planning requirements. To address this issue, we propose a new decision-making method for the selection of roadside non-point pollution reduction facilities. The principal factors informing the proposed decision-making methods are the road characteristics, including location, structure, number of lanes, and traffic volume. As a result of the study, a total of new pollution reduction plans were developed, with their selection conditions and the corresponding applicable facilities established. The effectiveness of the proposed pollution reduction schemes was demonstrated for roads in Kyounggi-do, providing a valuable basis for future pollution reduction plans.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.

• 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.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.06b
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• pp.197-226
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• 2003

터널에서의 암반분류/평가는 지보패턴결정 뿐만 아니라 터널주변암반에 대한 설계정 수 산정 및 물성평가에 있어 매우 중요한 요소라 할 수 있다. 암반분류는 각 국 또는 주요기관 별로 분류안이 만들어져 있으며, 현재 RMR분류와 Q-system이 가장 활발히 적용되고 있다. 본고에서는 터널설계단계에서 암반분류방법과 지보패턴결정과정을 고찰하였으며, 도로설계를 중심으로 적용현황을 분석하였다 또한 실제 터널시공시 암반분류 및 판정에 의한 지보공 변경사례를 살펴봄으로서 시공 중 암반분류/평가의 의미를 고찰하였다. 그리고 암반분류요소들에 대한 통계분석을 실시하여 암반분류요소들간의 상관관계를 분석하였다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1405-1408
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• 2011

도로의 일부구역을 일반시민이 특정목적으로 사용하기 위한 도로점용허가는 국민생활 및 재산권과 밀접한 관계가 있으나 설계도 등 구비서류가 다양하고 복잡하여 민원인이 신청하기에 불편하고, 이는 곧 민원행정에 대한 국민 신뢰도 저하로 이어지고 있다. 도로점용허가는 서류가 대부분 종이로 제출되어 보관 및 자료찾기 등 사후관리가 어렵고 민원 대응 등 업무처리가 힘들어 행정효율성 저하를 초래하고 있다. 본 연구는 도로점용허가 전반을 지원할 수 있는 온라인시스템(이하 '도로점용시스템'이라함)을 구축하기 위한 정보분류체계를 개발하여 대국민 서비스 및 업무효율성 향상을 도모하고자 한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.345-346
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• 2018

차종 별 교통량 자료는 도로의 유지관리나 분석 등의 행정 처리 업무에 필요한 기본 자료임과 동시에 각종 연구에 활용된다. 본 시스템은 그 일환으로서 화물차나 일반차량을 구분하여 특정 도로의 화물차 비율이나 교통량을 파악하는데 활용할 수 있다. 머신 러닝 알고리즘 중에서 높은 성능을 보이는 Support Vector Machine (SVM) 알고리즘을 이용하여 도로 위의 일반차량과 화물차를 구분하였다. 우선, 화물차와 일반차량의 차이를 구분하고자 각각의 영상에 대해 Histogram of Oriented Gradients (HOG) 기반 특징점을 추출하고 이에 따라 1 차원 벡터로 표현된 데이터를 SVM 으로 분류하여 구분한다.

• 한국도로학회지:도로
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• v.8 no.3 s.29
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• pp.43-51
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• 2006

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.269-276
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• 2008

Recently, constructing the database of road network is a main key in various social operation as like the transportation, management, security, disaster assesment, and the city plan in our life. However it need high expenses for constructing the data, and relies on many people for finishing the tasks. This study proposed the classification method for discriminating between the road and building points using the entropy theory, then detects the classes as a expecting road from the classified point group using the standard reflectance intensity of road and the characteristics restricted by raw. Hence the main object of this study is to develop a method which can detect the road in urban area using only the LiDAR data.