• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.28-38
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• 2001

Traffic information can be broadly categorized into point information and spatial information. Point information can be obtained by chocking only the presence of vehicles at prespecified points(small area), whereas spatial information can be obtained by monitoring large area of traffic scene. To obtain spatial information by image processing, we need to track vehicles in the whole area of traffic scene. Image detector system based on global tracking consists of video input, vehicle detection, vehicle tracking, and traffic information measurement. For video input, conventional approaches used auto iris which is very poor in adaptation for sudden brightness change. Conventional methods for background generation do not yield good results in intersections with heave traffic and most of the early studies measure only point information. In this paper, we propose user-controlled iris method to remedy the deficiency of auto iris and design flame difference-based background generation method which performs far better in complicated intersections. We also propose measurement method for spatial traffic information such as interval volume/lime/velocity, queue length, and turning/forward traffic flow. We obtain measurement accuracy of 95%∼100% when applying above mentioned new methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.182-194
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• 2017

Most of the drone (Unmanned Aerial Vehicle) research in terms of traffic management involves detecting and tracking roads or vehicles. The purpose of analyzing image footage in the transportation sector is to overcome the limitations of the existing traffic data collection system (vehicle detectors, DSRC, etc.). With regards to this, drones are the good alternatives. However, due to limitation in their maximum flight time, they are appropriate to use as a complementary rather than replacing the existing collection system. Therefore, further research is needed for utilizing drones for transportation analysis purpose. Traffic problems often arise from one particular section or a point that expands to the whole road network and drones can be fully utilized to analyze these particular sections. Based on the study on the uses of traffic survey analysis, this study is conducted by extracting traffic flow parameters from video images(range 800~1000m) of highway unit segments that were taken by drones. In addition, video images were taken at a high altitude with the development of imaging technologies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.2 s.3
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• pp.55-64
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• 2003

This research is about executing the suitability evaluation of the data through wireless communication system in comparison with the data through the wire system used in National Highway Traffic Management System. The communication system for the evaluation was established on Peoyngteak-Anjung section(about 19 Km) at Route 38 in southern National Highway of the National Capital region where image detectors with the communication by wire were set up. In the situation, the comparison of the data transmission rates between the wire system and the proposed system was accomplished. Before we evaluate the possibility of application about the proposed system, the elements of wireless systems performed the spot investigations. Traffic data transmission test among the equipments was accomplished on the section of the Route 38 where wireless communication equipments were set up. And then, the test of traffic data transmission between the field equipment and the data collective center of wireless communication system was carried out. It evaluated the reliability of wireless communication data for the suitability evaluation of cable/wireless data collected while 73 days.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.71-83
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• 2003

An arterial street control is performed for the purpose of the progression of a traffic flow using the arterial. However during the progression in the arterial, the change according to the time is one of the most representative problems occurring at a signal plan. This paper intends to efficiently operate the arterial progression by applying fuzzy logic, which is thought to be the most possible one in the inference as that of the human logic, to the traffic responsive control system. Fuzzy Logic controller is appliable to the daily human language (linguistic). can be dealt with the uncertain traffic data and is useful on planning the signal control to sensitively confront the randomly changing traffic condition. This study, based on the signal control part of the isolated intersection in "A Development of a Real-time, Traffic Adaptive Control Scheme Through VIDs"(Seong Ho. Kim. 1996). suggested the strategy for the progression control in the arterial and analyzed its effect by comparing the effect of the existing control method. In addition, the study compared each effect by using TRAF-NETSIM which is the traffic simulation software to analyze each control method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.26-36
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• 2021

In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.66-83
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• 2021

Vehicles experience changes in driving behavior due to the various facilities on the freeway. These sections may cause repetitive traffic congestion when the traffic volume increases, so safety issues may be raised. Therefore, the purpose of this study is to perform microscopic traffic analysis on these sections using drone images and to identify the causes of traffic problems. In the case of drone image, since trajectory data of individual vehicles can be obtained, empirical analysis of driving behavior is possible. The analysis section of this study was selected as the weaving section of Pangyo IC and the sag section of Seohae Bridge. First, the trajectory data was extracted through the drone image. And the microscopic traffic analysis performed on the speed, density, acceleration, and lane change through cell-unit analysis using Generalized definition method. This analysis results can be used as a basic study to identify the cause of the problem section in the freeway. Through this, we aim to improve the efficiency and convenience of traffic analysis.