• Title/Summary/Keyword: road classification

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A Study on Revising Construction Cost Calculation for Road Paving Maintenance Work (도로포장 유지보수 공사비산정기준 개정에 대한 연구)

  • Oh, Jae-Hoon;An, Bang-Yul
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2020.06a
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    • pp.155-156
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    • 2020
  • Unlike new construction projects, road paving maintenance work shows large productivity discrepancies depending on the conditions of the worksite. The current construction cost calculation scheme, however, only provides daily construction volume categorized by work scope and scale: There are no detailed standards that can be implemented on various types of worksites. To develop standards that enable the calculation of appropriate construction costs by taking into account worksite conditions, the current study conducted on-site surveys and interviews. The on-site research and analysis revealed that location, construction width, the day's worksite lot, work scope, and construction objectives were found to cause differences in construction volume. In addition to the existing work scope and work scape variables, the current study added weight constants reflecting the daily work volume based on movement conditions at site and the size of the worksite lot. In this process, the current study found that even one type of construction project can have fifteen different levels of daily construction volume. Such detailed classification was deemed to enable the proper calculation of construction costs based on worksite conditions.

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A Study on the Characteristic of Block Plan related to the site type of Elementary Schools (초등학교(初等學校) 입지유형(立地類型)에 따른 배치특성(配置特性)에 관한 연구(硏究) - 1998년(年) 이후(以後) 광주광역시(光州廣域市)에서 발주된 당선작(當選作)을 중심(中心)으로 -)

  • Oh, Sang-Mok;Oh, Sai-Gyu
    • Journal of the Korean Institute of Educational Facilities
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    • v.10 no.4
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    • pp.31-41
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    • 2003
  • The purpose of this study is to analyze the Characteristic of Block Plans related to site types of Elementary Schools in Kangju city. With analysis of 17 elementary school plans, we reached same conclusions. First, there is a possibility of seeing 4 examples that contiguity road condition and proportion of site, neighborhood park location, district units planning, which as classification the condition to feature of location. But the condition which is considered actually from Block Plan appeared with only contiguity road condition and district units planning two onlines. Second, despite there are elements having the possibility effecting to Block Plan, some arrangement forms are repeatedly used. There is a possibility of searching for that cause that it falls in mannerism of space form, type and place when it follows the arrangement form of existing defined as school.

Developing the Traffic Accident Prediction Model using Classification And Regression Tree Analysis (CART분석을 이용한 교통사고예측모형의 개발)

  • Lee, Jae-Myung;Kim, Tae-Ho;Lee, Yong-Taeck;Won, Jai-Mu
    • International Journal of Highway Engineering
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    • v.10 no.1
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    • pp.31-39
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    • 2008
  • Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis, developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(D), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

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Utilizing Visual Information for Non-contact Predicting Method of Friction Coefficient (마찰계수의 비접촉 추정을 위한 영상정보 활용방법)

  • Kim, Doo-Gyu;Kim, Ja-Young;Lee, Ji-Hong;Choi, Dong-Geol;Kweon, In-So
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.4
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    • pp.28-34
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    • 2010
  • In this paper, we proposed an algorithm for utilizing visual information for non-contact predicting method of friction coefficient. Coefficient of friction is very important in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. This method is composed of learning group(experiment, grouping material) and predicting friction coefficient group(Bayesian classification prediction function). Every group include previous work of vision. Advantage of our algorithm before entering such terrain can be very useful for avoiding slippery areas. We make experiment on measurement of friction coefficient of terrain. This result is utilized real friction coefficient as prediction method. We show error between real friction coefficient and predicted friction coefficient for performance evaluation of our algorithm.

Development of Vehicle Classification Algorithm using Non-Contact Treadle Sensor for Toll Collect System (통행료징수시스템을 위한 무접점 답판 방식의 차종분류 알고리즘 개발)

  • Seo, Yeon-Gon;Lew, Chang-Guk;Lee, Bae-Ho
    • The Journal of the Korea institute of electronic communication sciences
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    • v.11 no.12
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    • pp.1237-1244
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    • 2016
  • Vehicle classification system in domestic tollgates is usually to use treadle sensor for calculating wheel width and tread of the vehicle. Due to the impact that occurs when the wheels of the vehicle contact, treadle sensor requires high durability. Recently, KHC(Korea Highway Corporation) began operating high-speed lane for cargo truck. High-speed cargo truck generate more impact the design criteria of previous treadle. Therefore, an increase in the maintenance and management costs of the treadle damage is concerned. In this paper, we propose an algorithm to classify vehicles using non-contact treadle sensors for improving durability from physical impacts. This was based on the KHC's classification criteria and showed a classification accuracy of 99.5 % in one experiment with 1892 vehicles through Changwon tollgate in 1020 local road. Therefore, it shows that vehicle classification system using non-contact treadle sensor could be applied to domestic toll tollgates, effectively.

KOMPSAT-3A Urban Classification Using Machine Learning Algorithm - Focusing on Yang-jae in Seoul - (기계학습 기법에 따른 KOMPSAT-3A 시가화 영상 분류 - 서울시 양재 지역을 중심으로 -)

  • Youn, Hyoungjin;Jeong, Jongchul
    • Korean Journal of Remote Sensing
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    • v.36 no.6_2
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    • pp.1567-1577
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    • 2020
  • Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

A Comparative Study on Statistical Clustering Methods and Kohonen Self-Organizing Maps for Highway Characteristic Classification of National Highway (일반국도 도로특성분류를 위한 통계적 군집분석과 Kohonen Self-Organizing Maps의 비교연구)

  • Cho, Jun Han;Kim, Seong Ho
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.3D
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    • pp.347-356
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    • 2009
  • This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

Two-wheeler Detection using the Local Uniform Projection Vector based on Curvature Feature (이진 단일 패턴과 곡률의 투영벡터를 이용한 이륜차 검출)

  • Lee, Yeunghak;Kim, Taesun;Shim, Jaechang
    • Journal of Korea Multimedia Society
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    • v.18 no.11
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    • pp.1302-1312
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    • 2015
  • Recent research has been devoted and focused on detecting pedestrian and vehicle in intelligent vehicles except for the vulnerable road user(VRUS). In this paper suggest a new projection method which has robustness for rotation invariant and reducing dimensionality for each cell from original image to detect two-wheeler. We applied new weighting values which are calculated by maximum curvature containing very important object shape features and uniform local binary pattern to remove the noise. This paper considered the Adaboost algorithm to make a strong classification from weak classification. Experiment results show that the new approach gives higher detection accuracy than of the conventional method.

A study on the classifying vehicles for traffic flow analysis using LiDAR DATA

  • Heo J.Y.;Choi J.W.;Kim Y.I.;Yu K.Y.
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.633-636
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    • 2004
  • Airborne laser scanning thechnology has been studied in many applications, DSM(Digital Surface Model) development, building extraction, 3D virtual city modeling. In this paper, we will evaluate the possibility of airborne laser scanning technology for transportation application, especially for recognizing moving vehicles on road. First, we initially segment the region of roads from all LiDAR DATA using the GIS map and intensity image. Secondly, the segmented region is divided into the roads and vehicles using the height threshold value of local based window. Finally, the vehicles will be classified into the several types of vehicles by MDC(Minimum Distance Classification) method using the vehicle's geometry information, height, length, width, etc

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A Study on the Land Cover Classification and Facilities Management of Pusan Port using Satellite data (위성영상을 이용한 부산항만 주변지역 토지피복분류 및 시설물관리 구축 방안)

  • 이기철;김정희;이병환
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 1998.10a
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    • pp.59-65
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    • 1998
  • A thematic land cover map of Pusan port area was developed using Landsat satellite TM(Thematic Mapper) image. Two types of digital data which are road and sea water layer are extracted from existing paper map were overlayed over the developed land cover map. SPIN-2(KNR-1000) image was utilized to make a facility map of JaSungDae port. SPIN-2 image, which has a cell resolution of 1.56 m showed higer accuracy than TM image, which has a cell resolution of 30 m for facility mapping. Overall, the techniques of digital mapping using satellite image are very useful, effective and efficient.

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