• Title/Summary/Keyword: roads

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A Study on Road Traffic Volume Survey Using Vehicle Specification DB (자동차 제원 DB를 활용한 도로교통량 조사방안 연구)

  • Ji min Kim;Dong seob Oh
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.2
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    • pp.93-104
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    • 2023
  • Currently, the permanent road traffic volume surveys under Road Act are conducted using a intrusive Automatic Vehicle Classification (AVC) equipments to classify 12 categories of vehicles. However, intrusive AVC equipment inevitably have friction with vehicles, and physical damage to sensors due to cracks in roads, plastic deformation, and road construction decreases the operation rate. As a result, accuracy and reliability in actual operation are deteriorated, and maintenance costs are also increasing. With the recent development of ITS technology, research to replace the intrusive AVC equipment is being conducted. However multiple equipments or self-built DB operations were required to classify 12 categories of vehicles. Therefore, this study attempted to prepare a method for classifying 12 categories of vehicles using vehicle specification information of the Vehicle Management Information System(VMIS), which is collected and managed in accordance with Motor Vehicle Management Act. In the future, it is expected to be used to upgrade and diversify road traffic statistics using vehicle specifications such as the introduction of a road traffic survey system using Automatic Number Plate Recognition(ANPR) and classification of eco-friendly vehicles.

A Study on the Changes in the Back Garden of Gyeongbokgung Palace during Cheongwadae Period through an Interview with Landscape Manager (조경 관리자 인터뷰를 통한 청와대 시기 경복궁 후원의 변화에 관한 연구)

  • Kim, Kyu-Yeon;Lee, Shi-Young;Choi, Jaehyuck;Choi, Jong-Hee
    • Journal of the Korean Institute of Traditional Landscape Architecture
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    • v.41 no.2
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    • pp.26-34
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    • 2023
  • This study conducted interviews with former and current managers of Cheongwadae landscape architecture to provide basic information necessary to preserve, manage, and utilize Gyeongbokgung Palace's back garden, and the main conclusions summarized are as follows. First, the topography changed a lot with the construction of the main building and the official residence under President Roh Tae-woo. The water system was connected to Gyeongbokgung Palace in the past, but is now disconnected. Second, in the case of planting, the most important principles were the president's security and protocol, and accordingly, trees were placed or managed. Trees were planted by introducing excellent trees in various regions, and wildflowers and ground cover plants were frequently replaced according to the season. Third, facilities and roads were arranged for the president's protocol, hobbies, and workers' rest, and fire-fighting facilities were installed to prevent disaster in the forest area of Baegaksan Mountain. Fourth, the biggest inflection point of Gyeongbokgung Palace's back garden during Cheongwadae period was the change in topography due to the new construction of the main building and official residence during President Roh Tae-woo, the removal of A and B barbed wire fences that separated space during President Roh Moo-hyun, and the extensive landscaping carried out for the G20 Summit under President Lee Myung-bak. The area of Gyeongbokgung Palace's back garden is expected to face another inflection point due to the opening of Cheongwadae on May 10, 2022, and the work of evaluating the historical, academic, and landscape values of Gyeongbokgung Palace's back garden should be carried out while preserving the status.

An Overloaded Vehicle Identifying System based on Object Detection Model (객체 인식 모델을 활용한 적재불량 화물차 탐지 시스템 개발)

  • Jung, Woojin;Park, Yongju;Park, Jinuk;Kim, Chang-il
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.562-565
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    • 2022
  • Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

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Application of Linear Schedule Chart for Schedule Management of Linear Construction Project (선형시설물 공정관리 활용을 위한 선형공정표 활용 시스템 구축 방안)

  • Lee, Jaehee;Kang, Hyojeong;Kang, Leenseok
    • Korean Journal of Construction Engineering and Management
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    • v.24 no.2
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    • pp.13-23
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    • 2023
  • Unlike building construction projects, where the activity is repeatedly carried out in a limited area, civil engineering projects such as roads and railroads are carried out in a linear type in a horizontal working space over several tens of kilometers. Each activity is managed with a station number that has a unit of distance from the starting point to the end point. For this reason, since the work location information of the activity is a major management factor, the Gantt chart system that expresses only schedule information may have limitations. In this study, authors propose a method for constructing a linear schedule chart that can simultaneously express schedule information indicating the start and finish dates and location information indicating the start and end positions of each activity, and develop a system for generating a linear schedule chart. In the study, the coordinate axes of the linear schedule chart consisted of distance and date values on the X and Y axes, respectively, and each activity was expressed as a symbol that can infer the type of work to increase the visibility of the linear schedule chart compared to the simple bar chart method. The linear schedule chart generation system was reviewed for practical applicability by utilizing the actual schedule data of bridge structures in a railroad project.

Verification of Ground Subsidence Risk Map Based on Underground Cavity Data Using DNN Technique (DNN 기법을 활용한 지하공동 데이터기반의 지반침하 위험 지도 작성)

  • Han Eung Kim;Chang Hun Kim;Tae Geon Kim;Jeong Jun Park
    • Journal of the Society of Disaster Information
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    • v.19 no.2
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    • pp.334-343
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    • 2023
  • Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Sahagu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.

A Study on the Settlement Prediction of Soft Ground Embankment Using Artificial Neural Network (인공신경망을 이용한 연약지반성토의 침하예측 연구)

  • Kim, Dong-Sik;Chae, Young-Su;Kim, Young-Su;Kim, Hyun-Dong
    • Journal of the Korean Geotechnical Society
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    • v.23 no.7
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    • pp.17-25
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    • 2007
  • Various geotechnical problems due to insufficient bearing capacity or excessive settlement are likely to occur when constructing roads or large complexes on soft ground. Accurate predictions of the magnitude of settlement and the consolidation time provide numerous options of ground improvement methods and, thus, enable to save time and expense of the whole project. Asaoka's method is probably the most frequently used one for settlement prediction and the empirical formulae such as Hyperbolic method and Hoshino's method are also often used. To find an elaborate method of predicting the embankment settlement, two recurrent type neural network models, such as Jordan model and Elman-Jordan model, are adopted. The data sets of settlement measured at several domestic sites are analyzed to obtain the most suitable model structures. It was shown from the comparison between predicted and measured settlements that Jordan model provides better predictions than Elman-Jordan model does and that the predictions using CPT results are more accurate than those using SPT results. It is believed that RNN using cone penetration test results can be a highly efficient tool in predicting settlements if enough field data can be obtained.

A Study on Correlation Analysis between Inventory Data and Danger Grade of Cut Slopes: Cut Slopes in Kangwondo and Chungcheongdo. (절토사면 현황조사 자료와 위험도간의 상관분석에 관한 연구: 강원도, 충청도 일대 절토사면)

  • Kim, Jin-Hwan;Lee, Jeong-Yeob;Kim, Seung-Hyun;Koo, Ho-Bon
    • Journal of the Korean Geotechnical Society
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    • v.25 no.12
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    • pp.27-35
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    • 2009
  • KICT (Korea Institute of Construction and Technology) and KISTEC (Korea Infrastructure Safety and Technology Corporation) have been carrying out inventory survey on cut slopes along national roads since 2006. Unlike precision safety check, cut slope inventory survey is a simple check about cut slope's characteristics with the naked eye to collect the data base of slope maintenance. Inventory survey is classified into general status, cut slope characteristics and inspector opinions. The inventory data are analyzed to identify dangerous slopes and decide a safety ranking. In this paper, we performed a correlation analysis using SPSS (ver.15) about the 10,461 cut slope inventory data which are collected in Kangwondo and Chungcheongdo from 2006 to 2008. We calculated the correlation coefficient between cut slope inventory data and the danger score derived from the data. And we evaluated cut slope inventory data which have the more influence on the danger degree of cut slope. According to results of correlation analysis, we found that inventory data influencing cut slope danger degree are stuck and fallen rock, orientation of discontinuity and angle of upper slope. And these data are slightly different by regionally. Later on, if inventory research is finished, we will understand regional characteristics of cut slopes.

Establishment of a Estimation Model of On-Road and Off-Road Parking Demand Based on the Total Floor Area of Buildings (건축물 연면적에 따른 노상·노외 주차수요 산정 모형 구축)

  • Je mo Nam;Young woo Lee
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.2
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    • pp.44-53
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    • 2023
  • Recently, serious parking problems are occurring due to the difficulty of securing sufficient parking space, and it may lead to other traffic or social problems. In order to solve the parking problem in areas and districts beyond a certain range, a study on-roads and off-street parking lots reflecting regional characteristics is necessary. Therefore, this study establishing a parking demand calculation model for use as a basic study in establishing on-road and off-road characteristics. In order to conduct the study, Dong-fu, Daegu Metropolitan City was divided into dongs, and parking facilities and parking demand were investigated. The survey time was divided into daytime and nighttime on weekdays, and the types of vehicles were divided into three types: passenger car, small trucks and buses, large trucks and buses. As explanatory variables for calculating parking demand, the total floor area of buildings for each of six purposes was used, including detached houses, apartment houses, neighborhood living facilities, cultural and assembly facilities, business facilities, and sales facilities. As a result of the correlation analysis, among the six explanatory variables, the total area of neighborhood living facilities showed a significant correlation with on- and off-street parking demand. A regression analysis model was constructed using the total area of neighborhood living facilities as an explanatory variable, and statistically significant results were obtained.

Prediction of Safety Grade of Bridges Using the Classification Models of Decision Tree and Random Forest (의사결정나무 및 랜덤포레스트 분류 모델을 이용한 교량 안전등급 예측)

  • Hong, Jisu;Jeon, Se-Jin
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.3
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    • pp.397-411
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    • 2023
  • The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

A Research on Improving the Shape of Korean Road Signs to Enhance LiDAR Detection Performance (LiDAR 시인성 향상을 위한 국내 교통안전표지 형상개선에 대한 연구)

  • Ji yoon Kim;Jisoo Kim;Bum jin Park
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.3
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    • pp.160-174
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    • 2023
  • LiDAR plays a key role in autonomous vehicles, and to improve its visibility, it is necessary to improve its performance and the detection objects. Accordingly, this study proposes a shape for traffic safety signs that is advantageous for self-driving vehicles to recognize. Improvement plans are also proposed using a shape-recognition algorithm based on point cloud data collected through LiDAR sensors. For the experiment, a DBSCAN-based road-sign recognition and classification algorithm, which is commonly used in point cloud research, was developed, and a 32ch LiDAR was used in an actual road environment to conduct recognition performance tests for 5 types of road signs. As a result of the study, it was possible to detect a smaller number of point clouds with a regular triangle or rectangular shape that has vertical asymmetry than a square or circle. The results showed a high classification accuracy of 83% or more. In addition, when the size of the square mark was enlarged by 1.5 times, it was possible to classify it as a square despite an increase in the measurement distance. These results are expected to be used to improve dedicated roads and traffic safety facilities for sensors in the future autonomous driving era and to develop new facilities.