• 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.

• Journal of Korean Society of Transportation
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• v.14 no.4
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• pp.49-76
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• 1996

Until recently, the inductive loop detecters(ILD) have been used to collect a traffic information in a part of traffic manangment and control. The ILD is able to collect a various traffic data such as a occupancy time and non-occupancy time, traffic volume, etc. The occupancy time of these is very important information for traffic control algorithms, which is required a high accuracy. This accuracy may be improved by classifying a vehicle type with ILD. To classify a vehicle type based on a Analog Digital Converted data collect form ILD, this study used a typical and modifyed statistic method and General Learning Vector Quantization unsuperviser neural network model and a hybrid model of GLVQ and statistic method, As a result, the hybrid model of GLVQ neural network model is superior to the other methods.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.7-16
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• 2000

Traffic congestion has become severe in large metropolitan areas by the travel behavior of employees as the low rate of vehicle occupancy and the high rate of Private auto using. In order to relieve traffic congestion, central and local government plan to implement diverse transportation demand management strategies. The governments want to know what employment types and locations in different metropolitan areas lead to the highest rate of vehicle occupancy and Private auto use. This study suggest that in large metropolitan areas, the employment locations of urban and suburban as well as the employment type of service show low vehicle occupancy. In medium metropolitan sizes. low vehicle occupancies are observed in service employment as well as in the employment locations of CBD. CBD fringe. In small metropolitan areas, a low rate of vehicle occupancy exists in service employment as well as in the employment locations of urban and suburban areas. A high rate of auto use shows not only in basic employment but also in the employment locations of CBD and CBD fringe.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• 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.6
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• pp.68-77
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• 2013

This study suggested experimental study results of congestion detection method for intruding vehicle enforcement system. This congestion detection method is developed to determine optimal operation criteria of intruding vehicle enforcement system as detecting traffic congestion. In ITS sector, traffic management systems generally have used a sectional travel speed for congestion detection. However, image sensors have high error rate of congestion detection because of speed error. This study suggested comprehensive congestion detection criteria based on speed and occupancy rate using field studies. As field study results, the proposed intruding vehicle enforcement system using image sensor is capable of accurately detecting the traffic congestion using sectional speed of 20km/h and occupancy rate of 60% as congestion detection criteria.

• 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.

• 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.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.151-158
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• 2009

This study deals with the rear-end collisions in the rural aiea. The objectives of this study are 1) to analyze the characteristics of rear-end accidents of signalized intersections, and 2) to develop the accident models for Cheongju-Cheongwon. In pursing the above, this study gives the particular attentions to comparing the characters of urban and rural area. In this study, the dependent variables are the number of accidents and value of EPDO(equivalent property damage only), and independent variables are the traffic volumes and geometric elements. The main results analyzed are the followings. First, the statistical analyses show that the Poisson accident model using the number of accident as a dependant variable are statistically significant and the negative binomial accident model using the value of EPDO are statistically significant. Second, the independent variables of Poisson model are analyzed to be the ratio of high-occupancy vehicles, total traffic volume and the sum of exit/entry, and those of negative binomial regression are the main road width, total traffic volume and the ratio of high-occupancy vehicles. Finally, the specific independent variables to the rural area are the main road width, the ratio of high occupancy vehicle, and the sum exit/entry.

• 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.