• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.2
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• pp.12-22
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• 2010

The variable message sign(VMS) is a facility to smoothen traffic flows and enable safe passing by providing real-time necessary information on roads, weather, transportation, and traffic regulations. The VMS also solves a feeling of uneasiness and gives a sense of psychological security by providing information to drivers. However, the VMS has a strong character of being a non-market product but a public product, so it has not normally been evaluated for its value. This research has evaluated a value of satisfaction level for traffic information users, using a contingent valuation method(CVM). As a result of evaluating the value of satisfaction level for users through division into an urban roadway and an urban highway for the cities where an intelligent transportation system(ITS) has been established, the urban highway had a value of 96.7 won/system and the urban roadway had a value of 76.3 won/system.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.323-333
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• 2005

This paper shows disposal site about an airport using ICAO(International Civil Aviation Organization)and GIS(Geographic Information System). In Korea, mountains occupy above $70\%$ of the whole country centering around Baek-Du mountain range and a physical distribution system doesn't go on smoothly between east and west region. At present, disposal site about an airport follows the standard of ICAO and FAA(Federal Aviation Administration). So, it's desperately necessary to make the new standard suitable for the condition of Korea. This study analyzes major customers in the aerospace and transportation sector by considering the present condition of location of geography, atmospheric phenomena, population and transportation about Ul-Jin airport on the standard of ICAO and FAA. As the results, Gi-Sung site proposed for the airport using the existing a runway in air force has a locality road connecting a fishing and agrarian villages and is shown better than the others.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.57-57
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• 2023

최근 심각한 기후변화로 인한 호우, 태풍 등 기상현상의 변화로 다양한 재해가 발생하고 그로 인한 피해 규모도 커지고 있다. 현재 우리나라의 호우 재해에 대한 예보는 단순히 강수량, 강설량, 바람의 강도 등을 전달해 주고 있는데, 이러한 정보 전달의 형태는 그로 인한 피해 규모를 예측하기 어렵다. 본 시스템은 현재의 단순한 수치만을 보여주는 예보에서 호우가 어느 지역에 어떠한 영향을 미치는지에 대한 정보를 전달한다. 시간대별 격자단위(1km×1km)로 구획하여 그 영향이클 것이라고 예상되는 9개 분야(생활, 도로, 농업, 편의, 공업, 의료복지, 교육연구. 축산업, 공용)의 정보를 전달 해 줌으로써 경제적, 산업적 측면에서 재난으로 인한 피해를 최소화할 수 있도록 하였다. GIS와 호우위험영향도 분석결과를 제공하는 플랫폼이며 주요 기능은 종합위험등급 현황을한 눈에 볼 수 있는 GIS 대쉬보드 상황판과 IBH-HR(예측강우분석), IBF-G(수문분석), IBF-PRA(리스크 분석) 3개의 분석 모듈 그리고 분석 모듈을 통해 도출된 분석결과를 관리하는 ARM(분석이력관리)으로 구성되었다. 다양한 콘텐츠 서비스로 호우 영향정보의 활용성이 클 것으로 기대된다.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.69-75
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• 2015

Road facilities for safe driving were designed for drivers to distinguish them during day and night, but they cannot play their role when the weather becomes worse. Recently, the road facilities have been designed by using electric and electronic technology so that they can be displayed well at a long distance, but they should be replaced very often due to their frequent breakdown. So, there are many problems in traffic calming and maintenance. In this study, to solve the above problems, semi-permanent LED beacon light was installed in the area where traffic accident are frequent, and monitoring system was developed so that the LED beacon light can be maintenanced by connecting with system. For the above installation and development, system was based on window operating system and it was developed for worker to operate it by using P.C. through connecting with wireless local area network. The result of this study led to analyzing state information on the battery of field-installed LED beacon light in real time, and manegement to effectively by predicting their life cycle.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.263-263
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• 2023

블랙아이스는 눈에 쉽게 구분되지 않아 많은 교통사고를 초래하고 있다. 한국교통연구원 교통사고분석시스템에 따르면, 2017년부터 2021년까지 5년간의 서리/결빙으로 인한 교통사고 사망자는 122명, 적설로 인한 교통사고 사망자는 40명으로, 블랙아이스는 적설에 비해 위험성이 높은 것으로 나타난다. 과거의 다양한 연구에서 블랙아이스 생성조건을 기압과 한기 축적등의 조건에서 예측해왔지만, 이러한 기상학적 모델은 봄철 해빙기의 일교차로 인한 눈의 해동과 재냉각과 같은 다양한 기상 조건에서의 블랙아이스 탐지가 어렵다는 한계가 있어 최근에는 이미지 판별과 딥러닝모델(YOLO 등)을 기반으로 한 센서가 제시되고 있다. 그러나, 이러한 방법은 충분한 컴퓨팅 자원이 뒷받침되어야 하며, 블랙아이스 탐지까지 걸리는 속도가 빠르지 못한 편으로, 블랙아이스 초입 구간에서의 제동에 취약하다는 잠재적인 약점을 가지고 있다. 그러므로 본 연구에서는 블랙아이스의 주 원인인 서리나 어는비가 발생하기 위해서 주변 공기가 이슬점 온도 이하, 노면온도와 이슬점이 어는점보다 낮아야 함을 이용, IoT 센서 모듈을 통해 Magnus 방정식으로 계산한 이슬점 온도와 노면 온도를 사용하는 이동식 블랙아이스 추정 장치를 제시한다. 본 장치는 대기압, 온도, 습도로부터 계산된 이슬점 온도와 노면 온도를 통한 서리발생 가능성과 대기 온도, 노면 온도를 통해 어는비의 발생환경 여부를 계산한다. 본 연구 결과를 통해 블랙아이스 추정과 기상정보 생산을 동시에 가능케 하며, 추정 결과를 통합 수집서버에 전송함으로서 운전자에게 전방 블랙아이스 위험 구간을 조기에 전달하는 시스템과 이를 관리하기 위한 인프라를 구축하여 운전 시 결빙 미끄러짐 사고를 저감하고자 한다.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1534-1542
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• 2004

A novel vision-based scheme of extracting real-time traffic information parameters is presented. The method is based on a region classification followed by a spatio-temporal image analysis. The detection region images for each traffic lane are classified into one of the three categories: the road, the vehicle, and the shadow, using statistical and structural features. Misclassification in a frame is corrected by using temporally correlated features of vehicles in the spatio-temporal image. Since only local images of detection regions are processed, the real-time operation of more than 30 frames per second is realized without using dedicated parallel processors, while ensuring detection performance robust to the variation of weather conditions, shadows, and traffic load.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.47-54
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• 2006

In order to reduce traffic accidents, many researchers studied a traffic accident model. The Cause of traffic accidents is usually the mis calculation of traffic signals or bad traffic intersection design. Therefore, to analyse the cause of traffic accidents, it takes effort. This paper, it calculates the optimal safe car speed considering intersection conditions and weather conditions. It will recommend calculation of 1/3 in vehicle speed when there are rainy days and snow days. But the problem is that it will always display the same speed limit when whether conditions change. In order to solve these problems, in this paper, it is proposed the calculation of optimal safety speed algorithm uses weather conditions and road conditions. Computer simulations is prove that it computes the traffic speed limit correctly, which proposed considering intelligent traffic accident prediction algorithms.