• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.49-51
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• 2018

미국과 중국 그리고 일부 유럽국가에서는 교통혼잡 해결하기 위해 2인 이상 탑승한 차량만 운행 가능한 다인승 전용차로(HOV, High Occupancy Vehicle Lanes)를 도입하여 운영하고 있다. HOV를 도입한 도시에서는 나 홀로 운행 차량이 많이 감소 되어 교통 혼잡 문제를 조금이나마 해결 할 수 있었다. 현재 HOV에서는 차량 내부의 탑승 인원수를 확인하기 위한 시스템을 사용하고 있다. 기존의 해당 시스템은 HOV에 지나간 차량을 자동으로 적외선 카메라를 통해 촬영하여 사람이 직접 검수하는 방식이다. 기존 방식은 사람이 직접 검사하는 방식이라 이를 위한 많은 인력과 시간이 소모되는 점, 그리고 사람마다 확인한 결과가 다를 수 있는 등 여러 가지 단점이 있다. 본 논문에서는 기존 HOV의 차량 내부 탑승 인원 확인 기술의 여러 단점을 극복하기 위해 Deep Learning과 Computer Vision을 이용한 새로운 기술 설계를 위한 연구한 내용을 다룬다.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.125-129
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• 2005

As metropolitan areas are rapidly growing in both population and physical size, so too has the problem of traffic congestion. Magnifying this is the limited financial resources and lack of road corridor space available to juggle the many competing demands. High Occupancy Vehicle (HOV) facilities have been implemented in an attempt to alleviate the problem of growing congestion while considering the issue of limited funding and lack of physical space. HOV lanes may increase the efficiency of a road corridor by maximising its person carrying capacity. These facilities are meant to provide priority treatment to HOVs, thereby luring people to choose a transport mode with a higher occupancy than the single occupant vehicle (SOV), such as buses or carpools. This paper analyze the issues surrounding HOV lanes, their effect, problems and their evaluation by using Intergration, that is Traffic Simulation Software, when HOV lanes be implemented in the Olympic Highway.

• Journal of the Korea Society for Simulation
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• v.15 no.1
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• pp.19-25
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• 2006

As metropolitan areas are rapidly growing in both a lot of population and traffic volume, it causes traffic congestion. Generally, High Occupancy Vehicle (HOV) lanes may increase the efficiency of road usage. The main contribution in this paper is to provide the scientific attempt to measure the effectiveness with regard to HOV lanes adaptation using an Integration simulation tool in order to alleviate the traffic congestion in Olympic highway.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.239-241
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• 2022

Currently, in Korea, a multi-seater lane (HOV) and a designated lane system are being implemented to solve traffic congestion. However, in both systems, it is difficult to crack down on cases of violations without permission, so people are required to be assigned to areas that want to crack down. In this process, manpower and budget are inefficiently consumed. To compensate for these shortcomings, we propose the development of an integrated enforcement system through YOLO, a deep learning object recognition model. If the two systems are implemented and integrated using YOLO, they will have advantages in terms of manpower and budget over existing systems because only data learning and system maintenance are considered. In addition, in the case of violations in which it is difficult for the existing unmanned system to crack down, the effect of increasing the crackdown rate through continuous learning can be expected.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.67-77
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• 2022

An automatic freeway bus-only lane enforcement system was developed and assessed in a real-world environment. Observation of a bus-only lane on the Youngdong freeway, South Korea, revealed that approximately 99% of the vehicles violated the high-occupancy vehicle (HOV) lane regulation. However, the current enforcement by the police not only exhibits a low enforcement rate, but also induces unnecessary safety and delay concerns. Since vehicles with six passengers or higher are permitted to enter freeway bus-only lanes, identifying the number of passengers in a vehicle is a core technology required for a freeway bus-only lane enforcement system. To that end, infrared cameras and the You Only Look Once (YOLOv5) deep learning algorithm were utilized. For assessment of the performance of the developed system, two environments, including a controlled test-bed and a real-world freeway, were used. As a result, the performances under the test-bed and the real-world environments exhibited 7% and 8% errors, respectively, indicating satisfactory outcomes. The developed system would contribute to an efficient freeway bus-only lane operations as well as eliminate safety and delay concerns caused by the current manual enforcement procedures.