• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.181-190
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• 2022

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.277-283
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• 2021

Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.

• Journal of Korean Society of Transportation
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• v.17 no.3
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• pp.87-96
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• 1999

It is very important to analyze the capacity of basic freeway section in terms of planning, design, and operation of roads. Capacity, in Korean Highway Capacity Manual was Published in 1992, had been determined by data collected in only 4-lane freeway. But, Korea is continuously planning and constructing multi-lane roads for increasing traffic demand. For this reason, it is necessary to determine the capacity for multi-lane freeways. This study goals are two-fold the determination of lane capacity for 8-lane freeway; and the development of methodology determining total capacity using lane friction factor This study utilized the data collected by loop detectors at a station of FTMS in Kyungbu expressway. For determining capacities, this study analyzed maximum flow rate in various aspects such as collecting period, each lane, and total lane in one-way. In addition, this study evaluated the breakdown in traffic flow relations in order to find out interrelation of breakdown among lanes. Through this analysis, this study determined lane friction factors, critical speed, and critical density. Finally, this study expressed a new methodology of capacity analysis for multi-lane freeways using some findings from this analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.2 s.7
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• pp.13-22
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• 2005

The purpose of this research is how to estimate the traffic queue length in the signal intersection accurately. The current traffic queue length algorithm in COSMOS has been using the congestion diagram which comes from the speed of an average separated vehicle - using average vehicle length and the occupancy time from loop detectors. So some errors were occurred by the speed estimation method using average vehicle lengths. And Operators had been difficult to optimize some variables for measuring the traffic queue length estimation algorithm in COSMOS. Therefore the traffic queue length estimation algorithm on the basis of the relation between distances and occupancy rates from loop detectors was developed in this thesis. This thesis had the advantage of using occupancy rates which came out from loop detectors easily and no need to optimize some variables for the established algorithm in COSMOS. And the results of testing this algorithm in some sites which had installed COSMOS system showed better results than COSMOS system's results. But it was noted that further studies which carry it out in various sites and under various cases are necessary for applying to actual intersections.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.6
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• pp.127-132
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• 2008

Traffic intersection control is implemented by the data which is acquired to vechile loop detector. Traffic intersection control equation is Webster equation. which use passig and delayed vechile number. But webster equation is applied to the spot traffic intersection, it is not used to related traffic intersection network system. There is not the approprate remote traffic intersection control, even if there is, it is high cost local network system. Therefore low cost and expert traffic intersection control is realized by internet referencing next and distant intersection traffic information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.263-268
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• 1997

Software structure must be dynamic to support new geospatial information sources and their object-oriented implementation on the Web. JAVA is interactive, platform idependent and object-oriented language and meets all needs on Internet GSIS. This paper introduce JAVA based GIS program to manipulates various geographic data on Internet, communicating interactively and transfer real-time data between server and client. This and this program analysis roof detector in all part of Pusan area and indicates the traffic states, road surface conditions, weather information, shortest cut, and road names in JAVA client windows. Also this study shows various techniques in expression real time traffic informations.

• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.245-247
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• 2008

Traffic intersection conrol is implemented by the data which is acquired to vechile Loop detector. Traffic intersection control equation is Webster equation, which use passing and delayed vechile number. But webster equation is applied to the spot traffic intersection, it is not used to related traffic intersection network system. There is not the approprate remote traffic intersection control, even if there is, it is high cost local network system. Therefore low cost and expert traffic intersection control is realized by internet referencing next and distant intersection traffic information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.59-72
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• 2017

As the part of study which handles the measure to use the individual vehicle information of taxi GPS data on signal controls in order to overcome the limitation of Loop detector-based collecting methods of real-time signal control system, this paper conducted series of evaluations and improvements on link travel time, queue vehicle time estimates and traffic condition decision algorithm from the research introduced in 2016. considering the control group and the other, the link travel time has enhanced the travel time and the length of queue vehicle has enhanced the estimated model taking account of the traffic situation. It is analyzed that the accuracy of the average link travel time and the length of queue vehicle are respectably both approximately 95 % and 85%. The traffic condition decision algorithm reflected the improved travel speed and vehicle length. Smoothing was performed to determine the trend of the traffic situation and reduce the fluctuation of the data, and the algorithms have refined so as to reflect the pass period on overflow judgment criterion.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.79-88
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• 2009

The objective of this study is to develop the new vehicle classification algorithm and minimize classification errors. The existing vehicle classification algorithm collects data from loop and piezo sensors according to the specification("Vehicle classification guide for traffic volume survey" 2006) given by the Ministry of Land, Transport and Maritime Affairs. The new vehicle classification system collects the vehicle length, distance between axles, axle type, wheel-base and tire type to minimize classification error. The main difference of new system is the "Wandering" sensor which is capable of measuring the wheel-base and tire type(single or dual). The wandering sensor obtains the wheel-base and tire type by detecting both left and right tire imprint. Verification tests were completed with the total traffic volume of 762,420 vehicles in a month for the new vehicle classification algorithm. Among them, 47 vehicles(0.006%) were not classified within 12 vehicle types. This results proves very high level of classification accuracy for the new system. Using the new vehicle classification algorithm will improve the accuracy and it can be broadly applicable to the road planning, design, and management. It can also upgrade the level of traffic research for the road and transportation infrastructure.