• Journal of Korean Society of Steel Construction
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• v.26 no.5
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• pp.485-498
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• 2014

In this paper, vehicle-bridge interaction analysis under a series of moving vehicles to simulate a lane load was performed to estimate impact factor of the main cable, hanger and girder for the selected suspension bridges with 404m and 1545m main span. Korea Bridge Design Code(Limit State Design) was selected for the live model in which KL-510 truck was modeled 6-d.o.f. vehicle and a lane load was simulated by a series of single-axle vehicles. For the 404m main span bridge, hinge-type and floating-type girders at the tower were considered to examine the impact factor according to the connection and supporting type of the girders. The parameters considered herein are the types of live load-a truck only and a truck plus lane load, eccentricity of moving vehicles, road surface roughness and vehicle speed. The road surface roughness was randomly generated based on ISO 8608 and it was applied to the truck only. The impact factors were also evaluated by using the influence line method that is commonly used in cable-supported bridges and compared with those from vehicle-bridge interaction analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.4
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• pp.343-350
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• 2014

Divisive hierarchical clustering algorithms iterate the process of decomposition and clustering data recursively. In each recursive call, data in each cluster are arbitrarily selected and thus, the total clustering time can be increased, which causes a problem that it is difficult to apply the process of clustering neighbor vehicular position data in vehicular localization. In this paper, we propose a new heuristic algorithm for speeding up the clustering time by eliminating randomness of the selected data in the process of generating the initial divisive clusters.

• Journal of Korean Society of Steel Construction
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• v.10 no.2 s.35
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• pp.187-199
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• 1998

The highspeed railway bridge which support continuous and high moving mass evalute the dynamic state and make the displacement of the bridge makes more or less, but up to this time the bridges are designed by the static design concept. for example when we design bridge we use impact factor, which only times the static load makes dynamic load. But becouse it simples. it can't express all of the effects. And so, in this report we study the modeling method of the moving mass and the dynamic factor.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.179-187
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• 2009

This study develops a real-time signal control algorithm based on sectional travel times and includes a field test and evaluation. The objective function of the signal control algorithm is the equalization of delay of traffic movements, and the main process is calculating dissolved time of the queue and delay using the sectional travel time and detection time of individual vehicles. Then this algorithm calculates the delay variation and a targeted red time and calculates the length of the cycle and phase. A progression factor from the US HCM was applied as a method to consider the effect of coordinating the delay calculation, and this algorithm uses the average delay and detection time of probe vehicles, which were collected during the accumulated cycle for a stabile signal control. As a result of the field test and evaluation through the application of the traffic signal control algorithm on four consecutive intersections at 400m intervals, reduction of delay and an equalization effect of delay against TOD control were confirmed using the standard deviation of delay by traffic movements. This study was conducted to develop a real-time traffic signal control algorithm based on sectional travel time, using general-purpose traffic information detectors. With the current practice of disseminating ubiquitous technology, the aim of this study was a fundamental change of the traffic signal control method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7292-7301
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• 2014

With the development of IT Info-Communications technology, the vehicle with a combination of wireless-communication technology has resulted in significant research into the convergence of the component of existing traffic with information, electronics and communication technology. Intelligent Vehicle Communication is a Machine-to-Machine (M2M) concept of the Vehicle-to-Vehicle. The Vehicle-to-Infrastructure communication consists of safety and the ease of transportation. Security technologies must precede the effective Intelligent Vehicle Communication Structure, unlike the existing internet environment, where high-speed vehicle communication is with the security threats of a wireless communication environment and can receive unusual vehicle messages. In this paper, the Vehicle Identification number between the V2I and the secure message communication protocol was proposed using hash functions and a time stamp, and the validity of the vehicle was assessed. The proposed system was the performance evaluation section compared to the conventional technique at a rate VPKI aspect showed an approximate 44% reduction. The safety, including authentication, confidentiality, and privacy threats, were analyzed.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.119-128
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• 2008

On the many highways, severe traffic congestions happen chronically and make traffic problems like reduction of mobility because of rapid increase of vehicles though road construction has been last. In order to solve these traffic problems, it is needed to find the trend and the symptom of traffic congestion and to analyze the cause of congestion and the(spatial) range affected by congestion. To develop the traffic condition monitoring index prior to doing all those things is most important. With this reason, many countries including U.S. had been developed the congestion criteria and indices. In this paper, applicability and characteristics of existing traffic congestion indices were considered and the direction for development of a new traffic condition index was suggested to achieve an effective traffic management.