• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.600-607
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• 2011

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic identification becomes important for the effective use of network resources. In this paper, we present an Internet application traffic identification method based on flow correlation to overcome limitation of signature-based identification methods and to improve performance (completeness) of it. The proposed method can identify unidentified flows from signature-based method using flow correlation between identified and unidentified flows. We propose four separate correlation methods such as Server-Client, Time, Host-Host, and Statistic correlation and describe a flow correlation-based identification system architecture which incorporates the four separate methods. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.591-595
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• 2010

In this paper, a method that dynamically adapts the traffic flow of automated guided vehicles (AGVs) used in automated container terminals to the changing operational condition is presented. In a container terminal, the AGVs are vulnerable to traffic congestion because a large number of AGVs operate in a limited area. In addition, dynamically changing operational condition requires the traffic flow of AGVs to be continuously adjusted to keep up with the change. The proposed method utilizes a genetic algorithm to optimize the traffic flow. Exploiting the dynamic nature of the problem an approach that reuses the results of the previous search is tried to speed up the convergence of the genetic algorithm. The results of simulation experiments show the efficiency of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2473-2489
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• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.84-98
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• 1995

Network-based model were developed to predict short term future traffic volume based on current traffic, historical average, and upstream traffic. It is presumed that upstream traffic volume can be used to predict the downstream traffic in a specific time period. Three models were developed for traffic flow prediction; a combination of historical average and upstream traffic, a combination of current traffic and upstream traffic, and a combination of all three variables. The three models were evaluated using regression analysis. The third model is found to provide the best prediction for the analyzed data. In order to balance the variables appropriately according to the present traffic condition, a heuristic adaptive weighting system is devised based on the relationships between the beginning period of prediction and the previous periods. The developed models were applied to 15-minute freeway data obtained by regular induction loop detectors. The prediction models were shown to be capable of producing reliable and accurate forecasts under congested traffic condition. The prediction systems perform better in the 15-minute range than in the ranges of 30-to 45-minute. It is also found that the combined models usually produce more consistent forecasts than the historical average.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.15-24
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• 2022

At present, there are few studies on signal control of pedestrian traffic flow and non-motor traffic flow at intersections. Research on the optimization scheme of mixed traffic flow signal control can coordinate and control the overall traffic flow of pedestrians, non-motor vehicles and motor vehicles, which is of great significance to improve the congestion at intersections. For the traffic optimization of intersections, this paper starts from two aspects: channelization optimization and phase design, and reduces the number of conflict points at intersections from spatial and temporal right-of-way allocation respectively. Taking the classical signal timing method as the theoretical basis, and aiming at ensuring the safety and time benefit of traffic travelers, a channelization optimization and signal control scheme of the intersection is proposed. The channelization and phase design methods of intersections with motor vehicles, non-motor vehicles and pedestrians as objects are discussed, and measures to improve the channelization optimization of intersections are proposed. A multi-objective optimization model of intersection signal control was established, and the model was solved based on NSGA-II algorithm.

• Asia pacific journal of information systems
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• v.26 no.3
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.45-50
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• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.2
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• pp.131-139
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• 2003

The links operating interrupted flow are intend to yield the traffic between the out flow and inflow part effect zone of street section, we build the delay model using the time gap between under construction and not. We review the applicability of interrupted flow, and thus we can put this data to practical use as the basis data to compute the inducement charge for traffic delay. Also building about traffic effect zone of interrupted flow wouldn't produce at the section beside occupying roads and construction cross section, thus we must review the plan to minimize traffic delay by the construction occupying road. In future there must be advanced the incomplete in this study, and groping for the various alternatives to minimize the traffic delay by the road occupying construction, with developing the various sets of detailed analyzing models, that is analysis on the street strength, crossroads geometrical forms of crossroads, public traffics, pedestrians, occupying types.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.1061-1064
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• 2007

네트워크의 효율적인 관리를 위해서는 네트워크의 각 호스트에서 발생하는 트래픽을 실시간으로 모니터링 할 수 있는 시스템이 필요하다. 이러한 모니터링의 효율적인 방법 중 하나가 네트워크 장비에서 제공하는 flow 정보를 이용하는 방법이다. 하지만 이는 네트워크 장비의 과부하 발생, 운용비용 상승, 유연성 및 확장성 부족의 단점을 가진다. 이를 극복하기위하여 본 논문에서는 Enterprise 네트워크에 적합한 Flow 기반 실시간 트래픽 모니터링 시스템의 구조를 제안하고, 검증을 위해 구현한 내용을 기술한다. 본 시스템은 패킷을 수집하여 실시간으로 flow 정보를 생성하고 저장하는 Flow Generator 시스템, 저장된 flow 정보를 Analysis 시스템으로 전송하는 Flow Exporter 시스템, Traffic Analysis 시스템, 그리고 분석된 내용을 보여주는 Traffic Reporter 시스템으로 구성된다. 본 시스템은 다양한 분석 목적에 맞게 Flow 정보를 조절할 수 있는 유연성과 다양한 분석시스템을 구축할 수 있는 확장성을 가진다. 본 논문에서 기술한 시스템은 학교 Campus 네트워크를 대상으로 구축되었다.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.39-50
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• 2006

This study aims to introduce a basic principle to improve the incident detection algorithm using traffic flow diagrams that can classify traffic states with a high reliability on the basis of the analysis of traffic flow characteristics under the recurrent or incident congestions. It is tried to newly classify the traffic states with the speed-flow and speed-occupancy diagrams. This is because McMaster algorithm has a tendancy on not identifying the traffic states exactly using the flow-occupancy diagram. In this study it shows that the classification of traffic states is applicable to use speed-occupancy relationship Therefore, it is necessary to determine some parameters to correctly classify the areas representing the traffic states and it may be possible to develop a new algorithm to detect the incident with a high reliability.