• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.183-193
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• 2007

Traditionally, a congestion charge based on first-best congestion pricing theory, namely, the theory of marginal cost pricing theory, is equal to the difference between marginal social cost and marginal private cost. It is charged on each link so as to derive a user equilibrium flow pattern to a system optimal one. Based on this theory this paper investigates on the characteristics of first-best congestion pricing of multiple user class on road with variable demand, and presents two methods for analysis of social and spatial equity. For these purposes, we study on the characteristics of first-best congestion pricing derived from system optimal in time and in monetary unit, and analyze equity from this congestion pricing with an example network.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.101-104
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• 2000

Nowadays, the issue of congestion control in high-speed communication networks becomes critical in view of the bandwidth-delay products for efficient data flow. In particular, the fact that the congestion is often accompanied by the data flow from the high-speed link to low-speed link is important with respect to the stability of closed-loop congestion control. The Virtual-Connection Network (VCN) in Gigabit Ethernet networks is a packet-switching based network capable of implementing cell- based connection, link-by-link flow-controlled connection, and single- or multi-destination virtual connections. VCN described herein differ from the virtual channel in ATM literature in that VCN have link-by-link flow control and can be of multi-destination. VCNs support both connection-oriented and connectionless data link layer traffic. Therefore, the worst collision scenario in Ethernet CSMA/CD with virtual collision brings about end-to-end delay. Gigabit Ethernet networks based on CSMA/CD results in non-deterministic behavior because its media access rules are based on random probability. Hence, it is difficult to obtain any sound mathematical formulation for congestion control without employing random processes or fluid-flow models. In this paper, an analytical method for the design of a congestion control scheme is proposed based on Smith＇s principle to overcome instability accompanied with the increase of end-to-end delays as well as to avoid cell losses. To this end, mathematical analysis is provided such that the proposed control scheme guarantees the performance improvement with respect to bandwidth and latency for selected network links with different propagation delays. In addition, guaranteed bandwidth is to be implemented by allowing individual stations to burst several frames at a time without intervening round-trip idle time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.527-534
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• 2019

Several types of mathematical analysis methods are used for port waterway risk assessment based on marine traffic volume. In Korea, a marine traffic congestion model that standardizes the size of the vessels passing through the port waterway is applied to evaluate the risk of the waterway. For example, when marine traffic congestion is high, risk situations such as collisions are likely to occur. However, a scientific review is required to determine if there is a correlation between high density of maritime traffic and a high risk of waterway incidents. In this study, IWRAP Mk2(IALA official recommendation evaluation model) and a marine traffic congestion model were used to analyze the correlation between port waterway risk and marine traffic congestion in the same area. As a result, the linear function of R2 was calculated as 0.943 and it was determined to be significant. The Pearson correlation coefficient was calculated as 0.971, indicating a strong positive correlation. It was confirmed that the port waterway risk and the marine traffic congestion have a strong correlation due to the influence of the common input variables of each model. It is expected that these results will be used in the development of advanced models for the prediction of port waterway risk assessment.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3097-3107
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• 1999

Recently many studies have been done on the Random Early Detection(RED) algorithm as an active queue management and congestion avoidance scheme in the Internet. In this paper we first overview the characteristics of RED and the modified RED algorithms in order to understand the current status of these studies. Then we analyze the RED dynamics by investigating how RED parameters affect router queue behavior. We show the cases when RED fails since it cannot react to queue state changes aggressively due to the deterministic use of its parameters. Based on the RED parameter analysis, we propose a self-adaptive algorithm to cope with this RED weakness. In this algorithm we make two parameters be adjusted themselves depending on the queue states. One parameter is the maximum probability to drop or mark the packet at the congestion state. This parameter can be adjusted to react the long burst of traffic, consequently reducing the congestion disaster. The other parameter is the queue weight which is also adjusted aggressively in order for the average queue size to catch up with the current queue size when the queue moves from the congestion state to the stable state.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.10
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• pp.18-25
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• 2000

In this paper, we analyze TCP congestion control algorithm over ATM-UBR network. TCP congestion control algorithm consists of slow start, congestion avoidance, fast recovery, fast retransmit. We analyze the ATM-UBR network service using the BSD 4.3 TCP Reno, Vanilla. However we found the fact that the characteristic of fast retransmit, recovery algorithm makes a serious degradation of Performance in multiple losses of packets. We propose new fast retransmit, recovery algorithm to improve the problem. The results of performance analysis improve the multiple losses of packets using a proposed fast retransmit, recovery algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.289-296
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• 2015

Wireless multimedia sensor network senses and transfers mass multimedia data. Also, it is sensitive to latency. This thesis proposes a routing technique based on traffic priority in order to improve the network efficiency by minimizing latency. In addition, it proposes a congestion control mechanism that uses packet service time, packet inter-arrival time, buffer usage, etc. In this thesis, we verified the reduction of packet latency in accordance with the quality level of packet as a result of the performance analysis through the simulation method. Also, we verified that the proposed mechanism maintained a reliable network state by preventing packet loss due to network overload.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.187-193
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• 2012

Nash Equilibrium (NE) is as useful tool for investigating a participant's strategic generation quantity in a competitive electricity market. Cournot model may give a mixed strategy NE instead of a pure strategy when transmission constraints are considered. A mixed strategy is difficult to compute, complicated to understand conceptually, and hard to implement in an electricity market practically. This paper presents that a mixed strategy does not appear in Stackelberg leader-follower model even under a transmission congestion. A solution method is proposed for the leader-follower model under a nondifferentiable space of a strategy variable. Based on the pure strategy NE with a transmission line congested, the merit of leader-follower model is shown from a social welfare point of view.

• Proceedings of the KSAR Conference
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• 2004.06a
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• pp.186-186
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• 2004

In this study, we generated 40 somatic cell cloned (scNT) piglets. Of these, three displayed congestion in both liver and lung, and died within the first week of life. Two-dimensional gel electrophoresis experiments revealed changes in the responses of several detoxification-related proteins to stress and inflammation. As a result, congestive livers and lungs displayed extensive hepatopneumonic apoptosis.(omitted)

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.61-83
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• 2009

This study developed a variable demand traffic assignment model by stable dynamics. Stable dynamics, suggested by Nesterov and do Palma[19], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with the user equilibrium model, which is based on the arc travel time function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on congestion. It is therefore expected to be a useful analysis tool for transportation planners. In this study, we generalize the stable dynamics into the model with variable demands. We suggest a three stage optimization model. In the first stage, we introduce critical travel times and dummy links and determine variable demands and link flows by applying an optimization problem to an extended network with the dummy links. Then we determine link travel times and path flows in the following stages. We present a numerical example of the application of the model to a given network.