• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.119-128
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• 2008

Weather like rain, strong wind or snowfall may make the road condition deteriorated and sometimes induce traffic accidents, which lead to severe traffic congestion, thereby travelers may change their destinations elsewhere. Although origin-destination trip information is required to analyze transportation planning in urban area, there are little researches on the relationship between weather condition and travel patterns. This paper investigates the characteristics of travel patterns on expressway in rainy days of 2006. We compare the normal travel patterns with those of rainy days by the travel distance for each vehicle type. Results show that traffic volume and travel distance have been reduced in rainy days as we expect, and also show different travel patterns for weekday and weekend.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.278-287
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• 2022

With the development of technology, driving simulators have been used in various ways. In driving simulator experiments, scenario creation is essential to increase fidelity, achieve research aims, and provide an immersive experience to the driver. However, challenges remain when creating realistic scenarios, such as developing a database and the execution of scenarios in real-time. Therefore, to create realistic scenarios, it is necessary to acquire real-time data. This study intends to develop a method of acquiring real-time weather and traffic speed information for actual, specific roads. To this end, this study suggests the concatenator for dynamic data obtained from Arduino sensors and public open APIs. Field tests are then performed on actual roads to evaluate the performance of the proposed solution. Such results may give meaningful information for driving simulator studies and for creating realistic scenarios.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.131-140
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• 2012

Lane markings such as edgelines, centerlines, and lines that delineate lanes generally provide drivers with the various information for safe driving. Drivers can easily recognize the lane markings through the color differences between the markings and road surfaces during the daytime. However, it is a bit difficult for drivers to perceive them during the nighttime due to the lack of artificial lights. Although the glass beads with the 1.5-refractive index have been used to improve the visibility of the lane markings during the nighttime, it is still difficult for drivers to recognize the lane markings properly, especially during the rainy nighttime, which may often lead to traffic accidents. To improve the retroreflectivity and visibility of the lane markings during the rainy nighttime, the high refractive beads with the 2.4-refractive index are essentially required, but they do not work appropriately during the dry nighttime. Thus, the mixed materials with the 1.5, 1.9, and 2.4-refractive beads should be considered for the satisfactory implementation of the lane markings. This study reveals the best mixing rates of the beads by conducting benefit-cost analysis under various weather conditions in Korea. The analysis results show that the lane markings with the 100% of the 2.4-refractive beads provide the highest visibility of lane markings regardless of the roadway conditions, but the benefit-cost (B/C) ratio of the bead mixture is merely 0.46. The best mixing rate of the beads, from the highest B/C ratio viewpoint, was identified as the mixture with a 80% of 1.5-refractive beads and a 20% of 2.4-refractive beads. Some limitations and future research agenda have also been discussed.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.445-448
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• 2007

Korea Peninsula is exposed to landslide problems because large regions of Korea are composed of mountain. As a result, we have a great loss of life and property every year, such as road, bridge, and building. However, conventional survey has many restrictions of time and man power. In recent days, instead of field surveying, remote sensing has our attention for detecting damaged place. Synthetic Aperture Radar (SAR) provides the all-weather capability and complements information available. And through the 2-pass DInSAR technique, we can measure even very small displacement effect. In this study, we generated six interferograms of Kangwondo between 1992 and 1998, and estimated the vulnerable place for landslide.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1611-1617
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• 2013

Information about trafficability or the condition of road with regard to its being traveled over by vehicles is one of the most critical factors for roadway operation in winter. Specifically, when traveling on snowy or icy surfaces, the traction force varies per vehicle type including tire types, geometric characteristics of roads, and conditions of road surfaces. In general, front-wheel drive or four-wheel drive vehicles have better traction performance on snowy or icy surface than rear-wheel drive vehicles, and the latter type vehicle causes more serious traffic congestion when there is unexpected snowfall. Thus, traffic information regarding trafficability with respect to vehicle types, geometric characteristics of roadway sections, and roadway surface conditions can provide a foundation to make a decision whether to use the associated roadway sections for roadway operators as well as users. Based on the preceding premise, the objective of this study is to present a methodology for developing a trafficability index with respect to vehicle types, geometric characteristics of roadway sections, and roadway surface conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1164-1167
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• 2021

동절기, 야간 등 도로에서 결빙으로 인한 연쇄 추돌 사고는 교통 체증 및 2차 사고의 위험으로 이어진다. 도로 중 결빙 발생 다발 지역인 지방도로, 터널 출입구, 교량 구간, 산기슭 도로, 그늘진 곡선 도로를 대상으로 C-ITS 관점 안전운전 결빙 주의보 애플리케이션을 제공하여 결빙으로 발생하는 사고를 미리 예방하고자 한다. 노면/기상 상태를 아두이노, 기상 api로 측정, 차량 운전자용 앱(GIS/맵 기반) 구현을 통해 앱 사용 운전자 간 양방향 V2V, 운전자와 아두이노 센서 간 V2I 통신으로 결빙으로부터 운전자를 보호함에 있다.

• Journal of KIISE
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• v.42 no.2
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• pp.203-212
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• 2015

This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.87-97
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.123-133
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• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.

• 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.