• International Journal of Highway Engineering
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• v.17 no.5
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• pp.47-56
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• 2015

PURPOSES: The purpose of this study is to estimate the impact of variable message signage (VMS) on traffic safety as a function of road curve radius using statistical methods. METHODS: In order to analyze the impact of VMS installations on traffic safety, travel speed, lateral distance, and geometric data relating to road curvature in each study area was acquired and analyzed for the impact of providing VMS information on driver performance and traffic safety using statistical methods including student t-test, Mann-Whitney test, and the Anderson-Darling test for estimating traffic safety hazard zone in each lane. RESULTS: As a result of analyzing driver performance characteristics before and after providing VMS information, it was determined that by providing VMS information, mean travel speed is deceased and vehicles are driven with increased precision, following the centerline in the first and second lanes. Also the results of analyzing traffic safety impacts of VMS indicate that traffic safety performance factors in the first lane of the Gapyeong section can, on average, increase in the left and right side of the lane by 19.22% and 68.98%, respectively, and in the case of the second lane, safety impacts, on average, can increase in both sides by 100%. For the Hongcheon section, traffic safety impacts in the first lane, on average, can increase along the left and right sides of the lane by 32.31% and 47.18%, and within the second lane, traffic safety can be increased along the left and right side of the lane by 10.97% and -0.01%, respectively. CONCLUSIONS: Based on the results of this study, the impact on traffic safety obtained by providing VMS information for road sections with smaller curve radii is greater than can be obtained for road sections with larger curve radii.

• Journal of Korean Society of Forest Science
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• v.107 no.3
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• pp.287-293
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• 2018

This study was carried out to provide surface bearing capacity reinforcement of forest road by sub-base facilities based on a soft ground use of geosynthetics to prevent the damage of the road surface passing heavy logging trucks and to pass smoothly heavy truck against growing timber harvesting. The analysis of the road surface bearing capacity as progressing time and the increase of the number of passage of heavy logging trucks were conducted experimental section of forest road on the soft ground in the Forest Technology and Management Research Center. As a result, it was found that the road surface bearing capacity were stabilized at CBR of 15% or more, the effect of reinforcement by type of geosynthetics showed no significant difference after the lapse of about 1 year. After reaching the passage of 300 times for the heavy logging trucks on the sub-base construction section, the settlements was stabilized below the allowable standard of 50 mm, road surface bearing capacity also improved to more than CBR 20% and there was no significant difference in the thickness of the sub-base. However, in the section where the sub-base is not constructed, it is found that the lack of surface bearing capacity with the settlements more than the allowable standard is not possible to pass the heavy logging trucks. Therefore, in order to reinforce the road surface bearing capacity of the soft ground for the passage of the heavy logging trucks, it is necessary to construct a sub-base of at least 0.2 m when using geosynthetics.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.29-38
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• 2007

This study investigated the effects of delineation systems on drivers' maneuver and how the effectiveness of delineation system can be further improved according to the road geometry. The experiments were conducted to collect lateral placement data and detection distance data using GPS equipped vehicles. The main results are summarizedas follows. Firstly, installing the delineation facilities on the roads helps drivers to recognize road alignment. Secondly, the detection distance is longer for delineators than for raised pavement marker in tangent section, while there is no difference in curve section. The chevron show the longest detection distance in the curve section, while the raised pavement markers showed no distinctive performance in terms of detection distance and lateral placement. Therefore, we can recommend install delineators in the tangent sections and chevrons in curve sections, based on the analysis results of effects of delineation facilities.

• 한국도로학회:학술대회논문집
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• 2004.10a
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• pp.449-456
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• 2004

• Magazine of the Korea Concrete Institute
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• v.28 no.3
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• pp.46-49
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• 2016

• 한국도로학회:학술대회논문집
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• 1999.11a
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• pp.41-44
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• 1999

• 한국도로학회:학술대회논문집
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• 2008.10a
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• pp.471-478
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• 2008

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.17-28
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• 2024

In this study, the capacity of the tunnel and the general section was calculated and compared using the VDS detector data, and the decrease rate in capacity of the tunnel section was analyzed by tunnel type. To compare the capacity of the tunnel and the general section, the Product Limit Method (PLM) was applied to the VDS detector data. As a result of comparing the capacity of the tunnel and general section, the capacity of the tunnel section decreased by about 6.5% compared to the general section. To classify the tunnel type, the tunnel extension and the number of lanes were used as variables, and there was a difference in the decrease rate of capacity by tunnel group classified by each criterion.

• International Journal of Highway Engineering
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• v.18 no.5
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• pp.57-62
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• 2016

PURPOSES : The objective of this study is to evaluate the effect of size and depth of cavities on the pavement failure using the full-scale accelerated pavement testing. METHODS : A full-scale testbed was constructed by installing the artificial cavities at a depth of 0.3 m and 0.7 m from the pavement surface for accelerated pavement testing. The cavities were made of ice with a dimension of 0.5 m*0.5m*0.3m, and the thickness of asphalt and base layer were 0.2 m and 0.3 m, respectively. The ground penetrating radar and endoscope testing were conducted to determine the shape and location of cavities. The falling weight deflectometer testing was also performed on the cavity and intact sections to estimate the difference of structural capacity between the two sections. A wheel loading of 80 kN was applied on the pavement section with a speed of 10 km/h in accelerated pavement testing. The permanent deformation was measured periodically at a given number of repetitions. The correlation between the depth and size of cavities and pavement failure was investigated using the accelerated pavement testing results. RESULTS : It is found from FWD testing that the center deflection of cavity section is 10% greater than that of the intact section, indicating the 25% reduction of modulus in subbase layer due to the occurrence of the cavity. The measured permanent deformation of the intact section is approximately 10 mm at 90,000 load repetitions. However, for a cavity section of 0.7 m depth, a permanent deformation of 30 mm was measured at 90,000 load repetitions, which is three times greater than that of the intact section. At cavity section of 0.3 m, the permanent deformation reached up to approximately 90 mm and an elliptical hole occurred at pavement surface after testing. CONCLUSIONS : This study is aimed at determining the pavement failure mechanism due to the occurrence of cavities under the pavement using accelerated pavement testing. In the future, the accelerated pavement testing will be conducted at a pavement section with different depths and sizes of cavities. Test results will be utilized to establish the criteria of risk in road collapse based on the various conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.