• 농촌계획
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• 제11권1호
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• pp.1-8
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• 2005

The purpose of this study is to develop a simulation model of rural road path for infrastructure of green-tourism and public service in rural areas. This study makes an objective function for moving cost minimization considering car travel time according to road characteristics, which can route the optimal shortest road paths between the center places and all rear villages, based on GIS coverages of road-village network for connecting between center places and rural villages as input data of the model. In order to verify the model algorithm, a homogeneous hexagonal network, assuming distribution of villages with same population density and equal distance between neighborhood villages on a level plane area, was tested to simulate the optimal paths between the selected center nodes and the other rear nodes, so that the test showed reasonable shortest paths and road intensity defined in this study. The model was also applied to the actual rural area, Ucheon-myun, which is located on Hoengsung-gun, Kangwon-do, with 72 rural villages, a center village (Uhang, 1st center place) in the area, a county conte. (Hoengsung-eup, 2nd center place), and a city (Wonju, 3rd center place), as upper settlement system. The three kinds of conte. place, Uhang, Hoengsung-eup, and Wonju, were considered as center places of three scenarios to simulate the optimal shortest paths between the centers and rural villages, respectively. The simulation results on the road-village network with road information about pavement and width of road show that several spans having high intensity of road are more important that the others, while some road spans have low intensity of road.

• 대기
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• 제24권4호
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• Structural Engineering and Mechanics
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• 제11권3호
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• pp.259-272
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• 2001

The impact behavior of a multigirder concrete bridge under single and multiple moving vehicles is studied based on correlated road surface characteristics. The bridge structure is modeled as grillage beam system. A 3D nonlinear vehicle model with eleven degrees of freedom is utilized according to the HS20-44 truck design loading in the American Association of State Highway and Transportation Officials (AASHTO) specifications. A triangle correlation model is introduced to generate four classes of longitudinal road surface roughness as multi-correlated random processes along deck transverse direction. On the basis of a correlation length of approximately half the bridge width, the upper limits of impact factors obtained under confidence level of 95 percent and side-by-side three-truck loading provide probability-based evidence for the evaluation of AASHTO specifications. The analytical results indicate that a better transverse correlation among road surface roughness generally leads to slightly higher impact factors. Suggestions are made for the routine maintenance of this type of highway bridges.

• 제어로봇시스템학회논문지
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• 제21권2호
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• pp.87-94
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• 2015

This paper proposes a situation awareness method based on data fusion and independent objects for autonomous driving in on-road environment. The proposed method, designed to achieve an accurate analysis of driving situations in on-road environment, executes preprocessing tasks that include coordinate transformations, data filtering, and data fusion and independent object based situation assessment to evaluate the collision risks of driving situations and calculate a desired velocity. The method was implemented in an open-source robot operating system called ROS and tested on a closed road with other vehicles. It performed successfully in several scenarios similar to a real road environment.

• Smart Structures and Systems
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• 제24권6호
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• 한국도로학회논문집
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• 제10권4호
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• pp.247-255
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• 2008

현행 도로설계의 기준이 되는 "도로의 구조 시설 기준에 관한 규칙 해설 및 지침"에서는 설계속도에 따라 도로 선형별 최소 설계기준을 정하고 있으며, 이 기준을 만족시키면 교통안전성 이 확보되는 것으로 규정하고 있다. 이러한 설계기준에 적용되고 있는 개별 설계요소들은 원칙적으로 차량 및 운전자 특성을 감안하여 설치기준이 정립되었으나, 설계요소간의 연관성 또는 일관성에 대한 깊이 있는 연구를 통해 제반 기준이 정립된 것은 아니다. 결과적으로, 현재의 도로설계기준이 개별 설계 요소들의 조합에 의해 결정되는 도로의 안전성, 일관성 문제를 모두 고려하지는 못하였다. 따라서, 본 연구에서는 기존의 설계기준에 내재된 문제점을 인지하고 해결을 위해 우선적으로 3차원 가속도를 고려한 선형 설계방안에 대한 연구를 수행하였으며, 이를 통해 더욱 안전하고 조화로운 도로건설을 유도하고자 한다.

• 한국측량학회지
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• 제33권6호
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• pp.589-594
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• 2015

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

• 한국도로학회논문집
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• 제15권3호
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• pp.137-149
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• 2013

PURPOSES: This study is to suggest the Contribution of Road Capital in Industry and Optimal Level of Road Investment in South Korea METHODS: Based on the literature review, This research is empirically estimated using disaggregate and disaggregated data composed of 10-sectors covering the entire korea economy for the period 1970~2000. The relevant policy questions addressed in this report are : cost reduction and Scale elasticities of road, effect of road capital stock on demand for labor, capital and materials, marginal effect of road, industry TFP growth decomposition, Net Social Rates of Returns, optimal of road capital. RESULTS : The marginal benefits of the road capital at the industry level were calculated using the estimated cost elasticities. Demand for the road capital services varies across industries as do the marginal effects. The marginal benefits are positive for the principal industries. This suggests that for these industries the existing stock of road capital may be under supplied. CONCLUSIONS: This results emerges is that the ratio of the optimum to actual road capital, measured by road, was high at beginning of the period 1970s and declined 1990s. There appears to be evidence of under-investment in road capital. That is continuous and premeditated investment for road which lead to saving time and finance.

• Spatial Information Research
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• 제17권1호
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• pp.25-37
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• 2009

최근 인명과 재산을 보호할 수 있는 중요한 분야인 안전 운전 서비스를 위해 GIS 및 텔레매틱스 기술에 USN을 활용하는 연구가 증가하고 있다. 본 논문에서는 이러한 안전 운전 서비스를 위한 연구의 하나로, USN을 활용하여 굴곡 도로에서 발생할 수 있는 위험을 분석하고 사고를 예방하기 위한 기술을 제안한다. 위험 분석 기술은 크게 모델링과 알고리즘으로 구성된다. 모델링으로는 굴곡 도로, 도로 방향, 센서, 차량, 위험에 대한 모델을 제안하고, 알고리즘으로는 위험을 분석하고 경고할 수 있는 다중 레벨 위험 분석 알고리즘을 제안한다. 그리고, 제안한 모델링과 알고리즘에 대한 시뮬레이션 응용 프로그램을 구현한다.

• Journal of Positioning, Navigation, and Timing
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• 제9권1호
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• pp.23-31
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• 2020

It is essential to estimate the vehicle localization for an autonomous safety driving. In particular, since LIDAR provides precise scan data, many studies carried out to estimate the vehicle localization using LIDAR and pre-generated map. The road marking always exists on the road because of provides driving information. Therefore, it is often used for map information. In this paper, we propose to generate the Gaussian mixture map based on road-marking information and localization method using this map. Generally, the probability distributions map stores the single Gaussian distribution for each grid. However, single resolution probability distributions map cannot express complex shapes when grid resolution is large. In addition, when grid resolution is small, map size is bigger and process time is longer. Therefore, it is difficult to apply the road marking. On the other hand, Gaussian mixture distribution can effectively express the road marking by several probability distributions. In this paper, we generate Gaussian mixture map and perform vehicle localization using Gaussian mixture map. Localization performance is analyzed through the experimental result.