• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.97-114
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• 2022

Unlike ground roads, the on-ramp merging section of underground roads cannot be seen by drivers of main road due to tunnels. In this study, a driving simulator was used for analysis, and virtual driving experiments were carried out to assess the driver's visibility for different design factors. The driver's driving behavior was analyzed by setting scenarios considering the length of chevron markings and the approach speed from the main road. The results of the analysis were used to determine the design factors for ensuring visibility when constructing the virtual driving environment for each scenario. These factors, including speed, lane change points, and driver's gaze ratios, were reviewed for significance using a statistical approach. As a result, in scenarios with a higher approach speed from the main road, it was discovered that there was a difference in driver's behavior between specific scenarios depending on the length of the section with chevron markings. Based on these findings, this study suggests implications and feasible solutions to improve driver safety on the on-ramp merging section of underground roads.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.815-825
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• 2017

Visibility of lane marking is impaired at night or in the rain, which thereby threatens traffic safety. Recently, various studies and technologies have been developed to improve lane marking visibility, such as the extension of lane marking life expectancy (up to 1.5 times), improvement of lane marking equipment productivity, improvement of lane marking visibility by applying phosphorescent material mixed paint. Cost-benefit analysis was performed with considering various benefit items that can be expected. About 45% of traffic accidents would be prevented by improving lane marking visibility. Additionally, accident reduction benefit and traffic congestion reduction benefit were calculated as much as 246 billion KRW per year and 12 billion KRW per year, respectively, by reducing repaint cycle due to enhanced durability. 45 billion KRW per year is expected to reduced with improved lane detection performance of autonomous vehicle. Meanwhile, total increased cost when introducing phosphorescent material mixed paint to 91,195km of nationwide road is identified as 1922 billion KRW per year. However, economic feasibility could not be secured with 0.16 of cost-benefit ratio when applied to the road network as a whole. In case of "Accident Hot Spot" analyzing section window (400m), one or more fatality or two or more injured (one or more injured in case of less than 2 lanes per direction) per year were caused by pavement marking related accident, economic feasibility was secured. In detail, 3.91 of cost-benefit ratio is estimated with comparison of the installation cost for 5,697 of accident hot spot and accident reduction benefit. Some limitations and future research agenda have also been discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.89-97
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• 2013

Recently, economic and social damages are globally increased due to the heavy snowfall caused by global warming. To reduce the damages of sudden regional heavy snow in roads, suitable countermeasures should be established based on the accurate detection of snowfall intensity for each roadway segment. In this paper, we deal with snowfall intensity detecting algorithm in the road area from CCD Imagery. First, we determine the MLZ (MotionLess Zone), which does not contain lane markings and moving cars, in the image space. Next, snow streaks trespassing the MLZ are extracted with Canny operator and proposed algorithm. Also, the concept of SII (Snow Intensity Index), which is the number of snow streaks during one minute in the MLZ, is defined. Finally, the effectiveness of proposed algorithm is proved by visually comparing the imagery and SII value obtained during 69 minutes. In consequence, we figured out that the integration of SII is significantly related to an actual amount of snowfall.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.75-82
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• 2011

There is the uncertain period of the construction in case of the products meeting the quality standard of KS M 6080, the quality is degraded because of the abrasive loss of the paint caused by vehicle tires as the traffic amount increases and of the loss and detachment of the glass beads providing the retroreflective function. The abrupt degradation of visibility causes the high frequency of traffic accidents at night and increases the traffic accident rate. Additional supplementary construction induces the direct material and construction costs. As the more cost induction effect than the direct cost, the traffic jam caused by the additional construction increases the indirect social costs such as time cost and vehicle cost. Hence, the study is concerned with performing the abrasion resistance test based on the EN 1436 standard to check and improve the quality of various road marking materials resulting in improving the durability of road marking materials. However, even though the difference in the durability lifetime of resins(binders) is bibliographically or theoretically clear, there was no difference in the durability lifetime (retroreflectivity aspect) of the road marking paint using these binders. The reason is that the bonding of beads was very insufficient or that the cross density caused by crack or freshness was low. Moreover, the measured wet retroreflectivity was distributed as the Rw3 or higher class in average on the basis of EN 1436 but was very insufficient on the basis of the minimum wet threshold retroreflectivity with 100mcd/($m^2{\cdot}lx$) managed overseas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.921-929
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• 2015

Road conditions which obstruct the driver from seeing road markings, such as in the evening or in the rain, due to the lack of a light source gives way to increasing the risk of traffic accidents. In order to increase road safety, the Korea Expressway Corporation set up a pilot project which used high-quality materials along a section of the Gyeongbu Expressway between Daejeon and Pangyo. There is little research on high-quality materials in the field of pavement marking. This study introduces high-quality materials used in pavement materials, presents the results of a survey conducted to examine the effect of the pilot project and analyzes traffic accident data from before and after the implementation of the pilot project. The survey results show that 87% of the respondents were highly satisfied with the pilot project. With the goal of evaluating the effect of the pilot project, this study used traffic accident data provided by the Korea Expressway Corporation accident data and performed a before-after study of the number of traffic. The results of data analysis show that there were 62 and 48 traffic accidents before and after the implementation of the pilot project. In addition, the result of Emperical Bayes Method indicates that there is an 41.7% decrease in the number of traffic accidents as an effect of High-quality Pavement Marking Materials.