• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.27-30
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• 2021

경제 성장과 함께 자동차의 수요가 늘어남에 따라 교통사고 발생 빈도는 꾸준히 증가하고 있다. 이에, 본 연구에서는 교통사고를 야기하는 도로 및 기상환경과 같은 조건을 활용하여 기계학습 모델을 통해 서울시 교통사고 사상자 수를 예측하는 모형을 찾고자 한다. 활용한 데이터는 도로교통 공단에서 제공하는 교통사고 사상자 수 정보를 포함하는 데이터로 2015년부터 2018년도까지 데이터를 학습에 사용하였고 2019년도 데이터를 테스트 평가에 사용하였다. 실증연구를 통해 트리 기반의 모델 별 성능을 비교하였으며 본 연구에 대한 결과는 사고 발생 시 우선순위에 의한 구조활동이 가능하게 함과 도로상황 및 기상을 고려한 안전운전 가이드 지식으로 활용될 수 있다.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.610-618
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• 2013

The damaged district by flooding has been changed from mainly farmland to cities due to the weather phenomena which is different from the past. This has caused not only irreparable damage of people's lives and property but social infrastructures. There also exist serious damages such as isolation of drivers and traffic jam as the roads were flooded. In this study, we suggested a method to develop a flood risk index focused on not Si, Gun or Gu but roads. In addition, flood risk index in the roads just at the moment, when it rains quantitatively, will be provided by using real-time rainfall information provided by the Weather Center. Then it should be helpful to prevent people from being isolated by flooded roads in advance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.271-272
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• 2017

본 논문에서는 교통사고 데이터베이스 구축을 통해 교통사교 현황과 사고 당시의 여러 정황들을 파악할 수 있는 정보를 제공한다. 이 정보들에는 사고 당시의 기상상태, 도로형태, 차종, 연령, 성별 등의 데이터들이 포함되고 이러한 정보들을 바탕으로 데이터베이스 사용자들은 각 사고 별 종합적인 정보를 얻을 수 있다. 이를 통해 정부 당국 외에 보험사 등에 교통사고 관련 정책을 위한 유용한 정보들을 제공할 수 있다. 또한 운전자 개인들에게도 정보들을 제공해 교통사고를 보다 효율적으로 예방할 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.661-668
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• 2015

Them traffic accidents have been increased every year due to increasing of vehicles numbers as well as the gravitation of the population. The carelessness of drivers, many road weather factors have a great influence on the traffic accidents. Especially, the number of traffic accident is governed by precipitation, visibility, humidity, cloud amounts and temperature. The purpose of this paper is to analyse the effect of road weather factors on traffic accident. We use the data of traffic accident, AWS weather factors (precipitation, existence of rainfall, temperature, wind speed), time zone and day of the week in 2013. We did statistical analysis using logistic regression analysis and decision tree analysis. These prediction models may be used to predict the traffic accident according to the weather condition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.44-55
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• 2013

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.635-638
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• 1999

도로 위에 설치된 카메라에서 검지 영역의 데이터를 입력받아 통과 차량수, 도로 점유율, 차량속도, 차간 거리 등의 교통정보를 실시간으로 산출하는 기법은 지능형 교통정보 시스템(ITS)의 핵심 분야이다. 본 논문에서는 검지영역의 시공간 영상 분석에 의해 다양한 기상 조건과 그림자 등의 환경의 변화에 민감하지 않은 교통정보 산출기법을 제안한다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1601-1607
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• 2016

Recently, weather information has been increasingly used in various area. This study presents the necessity of hourly weather information for electricity demand forecasting through correlation analysis and multivariate regression model. Hourly weather data were collected by Meteorological Administration. Using electricity demand data, we considered TBATS exponential smoothing model with a sliding window method in order to forecast electricity demand. In this paper, we have shown that the incorporation of weather infromation into electrocity demand models can significantly enhance a forecasting capability.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.190-201
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• 2021

The variable message sign (VMS) is equipment to improve the efficiency of traffic flow and safety of travel by providing real-time information to road users on traffic, road, weather situation, and traffic control due to construction. The information messages of the letter-based VMS taking up most of the VMS on the general national highways consist of section, travel time in sections, and road control situation. This study devised an improvement plan centered on road users, not road managers-centered existing message-based VMS through an analysis of road users' preference targeting the letter-based VMS operated in the general national highways in the Seoul Metropolitan Area. By presenting the measure for system improvement through which information on unexpected situations that road users prefer the most can be quickly and efficiently provided, this study aims to improve the efficiency of road user-centered VMS traffic information.

• Journal of the Society of Disaster Information
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• v.14 no.2
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• pp.154-164
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• 2018

Purpose: Recent climate change makes weather-related disasters such as summer storms, heavy rains, winter snowfall disasters, and extreme cold temperature increase in trend. Heavy snowfall disasters requires speedy response due to various effects to traffic flows, buildings, and infrastructure. Heavy snowfall disaster response of South Korea is insufficient, even though heavy snowfall disasters affect urban safety. There have been lack of policy studies for heavy snowfall disasters. Method: This research analyzes case studies and explores implications using Information system technologies to snow removal vehicles and equipments for speedy snow removal during the heavy snowfall disasters. Results: Information system technology attachment to snow removal equipments can identify locations of snow removal vehicles and equipments for emergency period to support snow removal of adjacent jurisdictions. Conclusion: Case studies of this research can be further used for efficient application of snow removal tools of local governments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.366-369
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• 2008

무선 센서 네트워크(Wireless Sensor Network)는 차세대 IT 기술로서 소형, 저가, 저전력을 필요로 하며, 외부 환경의 모니터링과 제어기능을 수행할 수 있다. 이것은 소형 장치 안에 마이크로프로세서, 각종 센서, 액추에이터, 유 ${\cdot}$ 무선 통신 장치를 내장하는 수백 혹은 수천 개의 센서 노드로 구성된다. 본 논문에서는 이러한 센서 네트워크를 이용하여 기상의 악천 후 속에서 차량 및 도로 상황 정보를 실시간으로 미리 획득하고 분석하여 운전자에게 미리 도로의 안전속도를 통보할 수 있는 실시간 차량 안전속도 서비스 시스템을 설계하고 구현된 결과를 보여 주고자 한다. 본 시스템은 노면의 종류 및 기상 상태 등에 대한 정보를 수집하여 이를 바탕으로 운전자에게 안전 속도를 알려줌으로써 교통사고를 효과적으로 예방할 수 있는 방법을 제공할 수 있다.