• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.87-94
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• 2013

This study estimated the changes in delays and speeds of vehicles in exclusive bus lane and road when the High Occupancy Vehicles(HOV) use the median bus lane. Synchro simulation tool was used to optimize the traffic signal time on the network and VISSIM was applied to simulate various scenarios. Here, drivers behavior parameters in VISSIM was optimized using Simultaneous Perturbation Stochastic Approximation(SPSA) algorithm in order to represent real traffic condition. Based on the simulation results, the delay in Doan daero was decreased when the volume of HOV in current condition runs on the median bus lane, whereas delay in Doan dongro was increased in all scenarios. The changes in bus speed was not sharply decreased for both study sites, even though the number of HOV increased to 10%. Thus, it could be allowed that the HOV use the median bus lane in Doan dongro and Doan daero. Future research tasks include studying about changes in delay when the HOV use the curb bus lane.

• 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.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.19-38
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• 2002

To evaluate the effects of transportation system operation, usually measures of effectiveness(MOE) such as travel time, space mean speed, stop/delay ratio have been used. But, energy consumption as well as the existing MOE in transportation receives more attention as an alternative MOE in transportation operation. The purpose of this study is a development of procedure, which could measure the relative energy consumption for each alternative and compare the results. A mesoscopic simulator called INTEGRATION is used to evaluate the operation of high occupancy vehicle lane, signal optimization, lane expansion, and the application of ITS. Among those, the application of ITS shows the greatest effectiveness in energy reduction, and then lane expansion, signal optimization, and the operation of high occupancy vehicle lane in the order named. Because we don't consider the characteristics of vehicle class, Potential demand and the simulation time is just for an hour. it is recommended that a procedure for precise economic analysis and an improvement in methodology are needed in the future for the expanded application of this study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.120-122
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• 2021

Currently, as demand for cars at home and abroad increases, the number of vehicles is decreasing and the number of vehicles is increasing. This is the main cause of the traffic jam. To solve this problem, it operates a high-ocompancy vehicle (HOV) lane, a multi-passenger vehicle, but many people ignore the conditions of use and use it illegally. Since the police visually judge and crack down on such illegal activities, the accuracy of the crackdown is low and inefficient. In this paper, we propose a system design that enables more efficient detection using imaging techniques using computer vision to solve such problems. By improving the existing vehicle detection method that was studied, the trigger was set in the image so that the detection object can be selected and the image analysis can be conducted intensively on the target. Using the YOLO model, a deep learning object recognition model, we propose a method to utilize the shift amount of the center point rather than judging by the bounding box in the image to obtain real-time object detection and accurate signals.

• 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.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1026-1031
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• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.