• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.519-525
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• 2013

The traffic classification is a preliminary and essential step for stable network service provision and efficient network resource management. However, the payload signature-based method has a significant drawback in high-speed network environment that the processing speed is much slower than other method such as header-based and statistical methods. In this paper, We propose the server IP, Port cache-based traffic classification method using application traffic locality to improve the processing speed of traffic classification. The suggested method achieved about 10 folds improvement in processing speed and 10% improvement in completeness over the payload-based classification system.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2779-2789
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• 1999

In this paper, we propose a new neural Buffered Leaky Bucket algorithm for preventing the degradation of network performance caused by congestion and dealing with the traffic congestion in ATM networks. We networks. We justify the validity of the suggested method through performance comparison in aspects of cell loss rate and mean transfer delay under a variety of traffic conditions requiring the different QoS(Quality of Service). also, the cell scheduling algorithms such as DWRR and DWEDF used for multiplexing the incoming traffics are induced to get the delay time of the traffics fairly. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate is changed by the predicted values. The prediction of traffic loss rate by neural networks can effectively reduce the cell loss rate and the cell transfer delay of next incoming cells and be applied to other traffic control systems. Computer simulation results performed for traffic prediction show that QoSs of the various kinds of traffics are increased.

• Journal of Information Processing Systems
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• v.4 no.2
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• pp.41-52
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• 2008

In this paper, we discuss a novel reservation-based, asynchronous MAC protocol called 'Multi-rate Multi-hop MAC Protocol' (MMMP) for multi-hop ad hoc networks that provides QoS guarantees for multimedia traffic. MMMP achieves this by providing service differentiation for multirate real-time traffic (both constant and variable bit rate traffic) and guaranteeing a bounded end-to-end delay for the same while still catering to the throughput requirements of non real time traffic. In addition, it administers bandwidth preservation via a feature called 'Smart Drop' and implements efficient bandwidth usage through a mechanism called 'Release Bandwidth'. Simulation results on the QualNet simulator indicate that MMMP outperforms IEEE 802.11 on all performance metrics and can efficiently handle a large range of traffic intensity. It also outperforms other similar state-of-the-art MAC protocols.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.1-16
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• 2011

With the increasing number of vehicles in use in our daily life and the rise of traffic congestion problems, many methods and models have been developed for real time optimisation of traffic lights. Nevertheless, most methods which consider real time physical queue sizes of vehicles waiting for green lights overestimate the optimal cycle length for such real traffic control. This paper deals with the development of a generic hybrid model describing both physical traffic flows and control of signalised intersections. The firing times assigned to the transitions of the control part are considered dynamic and are calculated by a simplified optimisation method. This method is based on splitting green times proportionally to the predicted queue sizes through input links for each new cycle time. The proposed model can be easily translated into a control code for implementation in a real time control system.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.130-138
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• 2023

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.214-220
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• 2002

If neither a map, nor geographic-information processing equipment or a micro browser was prepared in a move terminal in the case of the old system, traffic guidance processing was not able to be carried out. Even if this system was required and did not carry out above -mentioned equipment or an above-mentioned system with a move terminal or a cellular phone, it could be made to do it. Traffic information guidance processing is possible to be completed only by the general telephone or electronic mail transmission.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.391-394
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• 2006

본 연구에서 영상 패턴인식 기술을 이용하여 교차로에서 발생하는 교통사고의 전과정을 동영상으로 기록하고 취득된 사고 자료를 교통관리센터에 전송하여 필요한 조치를 바로 취할 수 있도록 하는 시스템을 제시하였다. 제안된 기술에 따라 개발된 교통사고 자동기록장치가 서울시 교통사고 다발 교차로에 설치되어 운영 및 성능평가 중이다. 동 장치에서 수집된 교통사고 동영상 자료는 교통사고 조사신뢰도를 높이고 교통안전 개선에 크게 기여할 것이다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.40-52
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• 2011

This paper develops a traffic condition decision algorithm to improve the reliability of traffic information on urban streets. This research is reestablished the criteria of qualitative traffic condition categorization and proposed a new qualitative traffic condition decision types and decision measures. The developed algorithm can be classified into 9 types for qualitative traffic condition in consideration of historical time series of speed changes and traffic patterns. The performance of the algorithm is verified through individual matching analysis using the radar detector data in Ansan city. The results of this paper is expected to help promotion of the traffic information processing system, real-time traffic flow monitoring and management, use of historical traffic information, etc.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.40-46
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• 2020

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.307-322
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• 2010

Traffic classification is an essential task for network management. Many researchers have paid attention to initial sub-flow features based classifiers for traffic classification. However, the existing classifiers cannot classify traffic effectively in mobile IP networks. The classifiers depend on initial sub-flows, but they cannot always capture the sub-flows at a point of attachment for a variety of elements because of seamless mobility. Thus the ideal classifier should be capable of traffic classification based on not only initial sub-flows but also various types of sub-flows. In this paper, we propose a classifiable sub-flow selection method to realize the ideal classifier. The experimental results are so far promising for this research direction, even though they are derived from a reduced set of general applications and under relatively simplifying assumptions. Altogether, the significant contribution is indicating the feasibility of the ideal classifier by selecting not only initial sub-flows but also transition sub-flows.