• Title/Summary/Keyword: Lane marking

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Abrasion-Resistant Road Markings for Improved Durability Lane to Wear Simulators Test (차선재료의 내구성 향상을 위한 내마모성 시험 적용 연구)

  • Lee, Chang-Geun;Park, Jin-Hwan;Oh, Heung-Un
    • 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.

Effectiveness of the Peripheral Transverse Line as Speed-Reduction Treatment on Korean Expressway Ramps (고속도로 노면표시 감속유도시설 효과평가)

  • Lee, Seongkwan Mark;Lee, Kiyoung;Kim, Seokchool
    • International Journal of Highway Engineering
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    • v.14 no.6
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    • pp.85-92
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    • 2012
  • PURPOSES : Since expressways in South Korea are toll roads, many trumpet type interchanges exist, resulting in the installation of loop ramps very frequently. While the travel speed of the main lane is designed to be 100-110 km/h, the structure of a loop ramp is different and is designed for a minimum speed of 40 km/h. In fact, most of the actual travel speeds measured on the ramp exceed the designated speed, which has been a major problem in traffic safety. In this research, a type of pavement marking speed-reduction treatment called the "Peripheral Transverse Line" is installed on expressway loop ramps in order to study the change of driving speeds after the installation. METHODS : To verify statistically the change, this speed-reduction treatment has been installed on the Chungju interchange and the Yeoju junction. The driving speeds before the installation were compared with driving speeds both one month and five monthsafter the installation. RESULTS : As a result, the reductions of the average driving speeds after the treatment were statistically significant. More specifically, the average driving speeds of the Chungju interchange were reduced by 7.1-7.7 % for its tangent road section, and the speeds decreased by 8.5-9.5 % for its curve section. Similarly, in the Yeoju junction, an average speed reduction of 2.9-4.8 % for its tangent section was measured, along with 3.9% long-term speed reduction for its curve section. CONCLUSIONS : Since the pavement marking speed-reduction treatment has been partially proven to be effective from this research, we expect to expand this treatment and re-confirm the effect from a long-term perspective in the future.

Position of Stop Line according to the Left Turn Trajectory at Intersection (교차로 좌회전 궤적에 따른 정지선 위치에 관한 연구)

  • 김기용;김동녕
    • Journal of Korean Society of Transportation
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    • v.18 no.3
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    • pp.29-39
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    • 2000
  • Position of stop line according to left turn trajectory at intersections was studied. The Purpose of this study is to suggest a design guideline of left turn trajectory at intersections which is related to the Position of cross road stop line. Distance from the curb line to the stop line at each lane was calculated and discussed for various combination of road widths. Three design vehicle type and three control radii were considered. Setbacks of stop line from the curb line are Proportional to the control radius which depends on type of design vehicle. 8m, lim and 19m setback for control radius of 12m, 15m and 23m were calculated respectively as their maximum value. This result will be helpful to Paint the road marking on the Pavement which is usually difficult to fit to the designated radius. Field study of the effect of left turn trajectory on flow rate and safety was conducted. It was approved that improperly designed left turn trajectory decreases left turn capacity and increases conflicts among left turn flows of the adjacent lane.

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Automatic Construction of Deep Learning Training Data for High-Definition Road Maps Using Mobile Mapping System (정밀도로지도 제작을 위한 모바일매핑시스템 기반 딥러닝 학습데이터의 자동 구축)

  • Choi, In Ha;Kim, Eui Myoung
    • 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.