• Journal of Computing Science and Engineering
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• v.1 no.1
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• pp.31-55
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• 2007

Contemporary end-servers and network-routers rely on traffic shaping to deal with server overload and network congestion. Although such traffic shaping provides a means to mitigate the effects of server overload and network congestion, the lack of cooperation between end-servers and network-routers results in waste of network resources. To remedy this problem, we design, implement, and evaluate NetDraino, a novel mechanism that extends the existing queue-management schemes at routers to exploit the link congestion information at downstream end-servers. Specifically, NetDraino distributes the servers' traffic-shaping rules to the congested routers. The routers can then selectively discard those packets-as early as possible-that overloaded downstream servers will eventually drop, thus saving network resources for forwarding in-transit packets destined for non-overloaded servers. The functionality necessary for servers to distribute these filtering rules to routers is implemented within the Linux iptables and iproute2 architectures. Both of our simulation and experimentation results show that NetDraino significantly improves the overall network throughput with minimal overhead.

• IE interfaces
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• v.23 no.4
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• pp.349-356
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• 2010

The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.121-127
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• 2002

In this paper, we propose traffic management support and evaluate the performance through simulation. We suggest traffic management routing protocol that can guarantee reliance according to not only reduction of the Network traffic congestion but also distribution of the network load that prevents data transmission. For performance evaluation, we analyzed the average data reception rate and network load, considering the node mobility. We found that in the mobile Ad Hoc networks, the traffic management service increased the average data reception rate and reduced the network traffic congestion and network load in Mobile Ad Hoc Networks.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.135-142
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• 2010

QoS in the proxy system are under heavy influence from interferences such as congestion, latency, and retransmission. Also, multi-level streaming services affects from temporal synchronization, which lead to degrade the service quality. This paper proposes a new segment-based buffer management mechanism which reduces performance degradation of streaming services and enhances throughput of streaming due to drawbacks of the proxy system. The proposed paper optimizes streaming services by: 1) Use of segment-based buffer management mechanism, 2) Minimization of overhead due to congestion and interference, and 3) Minimization of retransmission due to disconnection and delay. This paper utilizes fuzzy value $\mu$ and cost weight $\omega$ to process the result. The simulation result shows that the proposed mechanism has better performance in buffer cache control rate, average packet loss rate, and delay saving rate with stream relevance metric than the other existing methods of fixed segmentation method, pyramid segmentation method, and skyscraper segmentation method.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.6-9
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• 2000

This paper presents the detailed derivation of optimal nodal price for active power to regionally evaluate system loss and congestion cost. The method is to decompose them into different components corresponding to system loss, transmission congestion, voltage constraint, and so on. The decomposed information for nodal price can be used to provide economic signals for generation or transmission investment as well as to improve the efficient usage of power grid and congestion management. The result of case study on IEEE 30 bus system is reported to illustrate the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.12
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• pp.567-572
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• 2005

This paper presents a scheme to design optimal placement of phase-shifting transformers for power system congestion problems. A good design of phase-shifting transformers placement can improve total transfer capability in interconnected systems. In order to find the optimal placement of phase-shifting transformers, the power flows of the interesting transmission lines are evaluated using sequential quadratic programming technique. This algorithm considers power balance equations and security constraints such as voltage magnitudes and transmission line capacities. The proposed scheme is tested in 10 machines 39 buses and IEEE 57 buses systems. Test result shows that the proposed method can find the optimal placement of phase-shifting transformers to solver power system congestion problems.

• 전자공학회논문지 IE
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• v.43 no.3
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• pp.41-48
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• 2006

In this paper, we propose a new active queue management (AQM) scheme by utilizing the predictability of the Internet traffic. The proposed scheme predicts future traffic input rate by using the auto-regressive (AR) time series model and determines the future congestion level by comparing the predicted input rate with the service rate. If the congestion is expected, the packet drop probability is dynamically adjusted to avoid the anticipated congestion level. Unlike the previous AQM schemes which use the queue length variation as the congestion measure, the proposed scheme uses the variation of the traffic input rate as the congestion measure. By predicting the network congestion level, the proposed scheme can adapt more rapidly to the changing network condition and stabilize the average queue length and its variation even if the traffic input level varies widely. Through ns-2 simulation study in varying network environments, we compare the performance among RED, Adaptive RED (ARED), REM, Predicted AQM (PAQM) and the proposed scheme in terms of average queue length and packet drop rate, and show that the proposed scheme is more adaptive to the varying network conditions and has shorter response time.