• The Journal of Bigdata
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• v.2 no.2
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• pp.129-140
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• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• International Journal of Highway Engineering
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• v.14 no.4
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• pp.141-148
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• 2012

PURPOSES : This study demonstrates the need for the collection of road weather information in order to perform efficient snow removal works during the winter season. Snow removal operations are usually dependent upon weather information obtained from the Automatic Weather Station provided by the Korea Meteorological Administration. However, there are some difference between road weather and weather forecasts in their scope. This is because general weather forecasts are focused on macroscopic standpoints rather than microscopic perspectives. METHODS : In this study, the relationship between snow removal works and historical weather forecasts are properly analyzed to prove the importance of road weather information. We collected both weather data and snow removal works during winter season at "A" regional offices in Gangwon areas. RESULTS : Results showed that the validation of weather forecasts for snow removal works were depended on the height difference between AWS location and its neighboring roadway. CONCLUSIONS : Namely, it appears that road weather information should be collected where AWS location and its neighboring roadway have relatively big difference in their heights.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.99-105
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• 2017

In this paper, we propose to design and implement the database management system for analyzing vehicle accidents through utilizing integration of the public big data. And the paper aims to provide valuable information for recognizing seriousness of the vehicle accidents and various circumstances at the accident time, and to utilize the produced information for the insurance company policies as well as government policies. For analysis of the vehicle accidents the system utilizes the integrated big data of National Indicator System, the Meteorological Office, National Statistical Office, Korea Insurance Development Institute, Road Traffic Authority, Ministry of Land, Infrastructure and Transport as well as the National Police Agency, which differentiates this system from the previous systems. The system consists of data at the accident time including weather conditions, vehicle models, age, sex, insurance amount etc., by which the database system users are able to obtain the integral information about vehicle accidents. The result shows that the vehicle accidents occur more frequently in the clear weather conditions, in the vehicle to vehicle conditions and in crosswalk & crossway. Also, it shows that the accidents in the cloudy weather leads more seriously to injury and death than in the clear weather. As well, the vehicle accident information produced by the system can be utilized to effectively prevent drivers from dangerous accidents.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.107-117
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• 2020

With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.221-228
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• 2020

As industrial technology advances, convenience is also being developed. Many people living in big cities are commuting using transportation such as buses, taxis, cars, etc. and enjoy leisure, so research is needed to reduce the damages caused by traffic accidents. This study deals with estimating road-level rainfall in real-time. A rainfall observation data and radar data provided by the Korea meteorological administration were collected in real-time to create an integrated database, which was estimated as road-level rainfall by universal kriging method. Besides, we conducted a study to interactively visualization of mash-up road traffic information in real-time with integrating rainfall information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.133-146
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• 2021

Providing weather information on roads today means that the road weather conditions near weather observation points are presented to road managers and road users. These weather observation points are managed by the Korea Meteorological Administration. However, it is difficult to provide accurate weather information due to physical limitations such as the presence of precipitation collection points, distance to weather information provision roads, and the presence of mountains. Therefore, this study intends to perform a comparative analysis by time zone and administrative dong provided by the Meteorological Administration using the wiper information among the information contained in the PVD(Probe Vehicle Data) collected from the highway C-ITS project. As a result of the analysis it was possible to detect rainfall even in the event of local rainfall and rainfall over a long period of time and the higher the cumulative precipitation per hour, the higher the probability of coincidence. This study is meaningful because it used PVD to solve the limitations of the existing road weather information provision method and suggested utilization plan for PVD.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.3-11
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• 2013

Under the consumption of bad weather situation affects traffic flows, the study scope is focused on highway capacity and speed variations among other highway traffic flow characteristic changes according to snowfall density. Thus, this study carried out through the data collection and statistical analysis by focusing on capacity and speed changes. Traffic volume, speed and density were selected as factors to explain the property change of a traffic flow for analysis, and 7 basic sections such as 3 highways in Gyeonggi-do and 4 highways near the meteorological observatory were selected as survey points for data collection. Snowfall levels were classified into 3 steps(Light, Medium, Heavy Snow) to analyze the capacity change by snowfall levels. As a result of analysis, the change of capacity depending on snowfall levels decreased 13.2% in case of light snow compared to a good weather, 18.6% in case of medium snow and 32.0% in case of heavy snow, so the capacity reduction rate increased as the snowfall level increased. The worsening weather appeared to have a very big possibility to act as a factor to reduce the operational efficiency of a road, so a road design and operation method considering this should be presented in the future.

• 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.