• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.19-28
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• 2015

In this paper, we proposed an algorithm for the identification of relieving or worsening current traffic congestion using historic traffic congestion patterns. Historical congestion patterns were placed in an adjacency list. The patterns were constructed to represent spatial and temporal length for status of a congested road. Then, we found information about historical traffic congestions that were similar to today's traffic congestion and will use that information to show how to change traffic congestion in the future. The most similar pattern to current traffic status among the historical patterns corresponded to starting section of current traffic congestion. One of our experiment results had average error when we compared identified changes of the congestion for one of the sections in the congestion road by using our proposal and real traffic status. The average error was 15 minutes. Another result was for the long congestion road consisting of several sections. The average error for this result was within 10 minutes.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1229-1233
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• 2010

In event-driven wireless sensor networks, network congestion occurs when event data, which have higher transmission rates than periodic sensing data, arc forwarded to bottleneck links. As the congestion continues, congestion collapse is triggered, so most of packets from source nodes are failed to transmit to a sink node. Rate control schemes can be a solution for preventing the congestion collapse problem. In this paper, a rate control scheme that each node controls child node's data rate based on congestion patterns is proposed. Experiments show that the proposed scheme effectively controls network congestion and successfully transmits more event data packets to a sink node than existing rate control schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.762-777
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• 2014

With the increasing usage of cloud applications such as MapReduce and social networking, the amount of data traffic in data center networks continues to grow. Moreover, these appli-cations follow the incast traffic pattern, where a large burst of traffic sent by a number of senders, accumulates simultaneously at the shallow-buffered data center switches. This causes severe packet losses. The currently deployed TCP is custom-tailored for the wide-area Internet. This causes cloud applications to suffer long completion times towing to the packet losses, and hence, results in a poor quality of service. An Explicit Congestion Notification (ECN)-based approach is an attractive solution that conservatively adjusts to the network congestion in advance. This legacy approach, however, lacks scalability in terms of the number of flows. In this paper, we reveal the primary cause of the scalability issue through analysis, and propose a new congestion-control algorithm called FaST. FaST employs a novel, virtual congestion window to conduct fine-grained congestion control that results in improved scalability. Fur-thermore, FaST is easy to deploy since it requires only a few software modifications at the server-side. Through ns-3 simulations, we show that FaST improves the scalability of data center networks compared with the existing approaches.

• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.139-149
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• 2004

As the expansion of road capacity has become impractical in many urban areas, congestion pricing has been widely considered as an effective method to reduce urban traffic congestion in recent years. The principal reason is that the congestion pricing may lead the user equilibrium (UE) flow pattern to system optimum (SO) pattern in road network. In the context of network equilibrium, the link tolls according to the marginal cost pricing principle can user an UE flow to a SO pattern. Thus, the pricing method offers an efficient tool for moving toward system optimal traffic conditions on the network. This paper proposes a continuous network design program (CNDP) in network equilibrium condition, in order to find optimal congestion toll for maximizing net economic benefit (NEB). The model could be formulated as a bi-level program with continuous variable(congestion toll) such that the upper level problem is for maximizing the NEB in elastic demand, while the lower level is for describing route choice of road users. The bi-level CNDP is intrinsically nonlinear, non-convex, and hence it might be difficult to solve. So, we suggest a heuristic solution algorithm, which adopt derivative information of link flow with respect to design parameter, or congestion toll. Two example networks are used for test of the model proposed in the paper.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.29-37
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• 2006

As a recurrent congestion of urban freeway occurs in almost same time and section, it is possible to manage the congestion effectively by the expectation and advance correspondence. In the existing traffic management system. we have used pattern data to manage a recurrent congestion. But it is not applicable to an urban freeway which kas various traffic circumstance. In this study, the probability by travel speed using a statistical distribution method will be used to predict the probability of recurrent congestion. It is expected that we can get the point of time and the duration of recurrent congestion, and we can devise an effective advance correspondence and a transportation operation.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.159-166
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• 2004

The capacity concept presented in the Highway Capacity Manual is for steady-state traffic flow assuming that there is no restriction in downstream flowing, which is traditionally used for planning, design, and operational analyses. In the congested traffic condition, the control objective should be to keep the congested regime from growing and to recover the normal traffic condition as soon as possible. In this control case, it is important to predict the spatial-temporal pattern of congestion evolution or dissipation and to estimate the throughput reduction according to the spatial-temporal pattern. In this context, the new concept of dynamic capacity for managing congested traffic is developed in terms of spatial-temporal evolution of downstream traffic congestion and in view of the 'input' concept assuming that flow is restricted by downstream condition rather than the 'output' concept assuming that there is no restriction in downstream flowing (e.g. the mean queue discharge flow rate). This new capacity is defined as the Maximum Sustainable Throughput that is determined based on the spatial-temporal evolution pattern of downstream congestion. And the spatial-temporal evolution pattern is estimated using the Newell's simplified q-k model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.813-819
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• 2004

This paper infers two patterns of timeout from the characteristics of TCP Reno and confirms the existence of the patterns by conducting actual experiments. All timeouts can be classified into either of these patterns by using the history of RTT value. Based on the observed patterns, the paper proposes two algorithms to improve the performance of TCP Reno. Experimental results show that, when compared with TCP Reno, the proposed algorithms improve the bandwidth utilization by 3 to 12 percent. The paper provides good examples of how timeout-based and delay-based congestion control can efficiently work together.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1881-1900
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• 2014

Since 802.11 wireless LANs are so widely used, it has become common for numerous access points (APs) to overlap in a region, where most of those APs are managed individually without any coordinated control. This pattern of wireless LAN usage is called the private OBSS (Overlapping Basic Service Set) environment in this paper. Due to frame collisions across BSSs, each BSS in the private OBSS environment suffers severe performance degradation. This study approaches the problem from the perspective of congestion control rather than noise or collision resolution. The retry limit, one of the 802.11 attributes, could be used for traffic control in conjunction with TCP. Reducing the retry limit causes early discard of a frame, and it has a similar effect of random early drops at a router, well known in the research area of congestion control. It makes the shared link less crowded with frames, and then the benefit of fewer collisions surpasses the penalty of less strict error recovery. As a result, the network-wide performance improves and so does the performance of each BSS eventually. Reducing the retry limit also has positive effects of merging TCP ACKs and reducing HOL-like blocking time at the AP. Extensive experiments have validated the idea that in the OBSS environment, reducing the retry limit provides better performance, which is contrary to the common wisdom. Since our strategy is basically to sacrifice error recovery for congestion control, it could yield side-effects in an environment where the cost of error recovery is high. Therefore, to be useful in general network and traffic environments, adaptability is required. To prove the feasibility of the adaptive scheme, a simple method to dynamically adjust the value of the retry limit has been proposed. Experiments have shown that this approach could provide comparable performance in unfriendly environments.