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Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.


Papers
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Journal ArticleDOI
TL;DR: A general framework of modeling the stochastic live load from traffic for a long-span bridge is developed and the cellular automaton (CA) traffic flow simulation technique is adopted to develop the analytical basis of the framework.

92 citations

Book ChapterDOI
04 Dec 2003
TL;DR: The task of operating a traffic network is cast as a distributed, stochastic game in which the agents solve reinforcement-learning problems, and results from computational experiments show that these agents can yield substantial gains with respect to the performance achieved by two other control policies for traffic lights.
Abstract: Automatic learning techniques stand as promising tools to respond to the need of higher efficiency of traffic network, even more so at times of mounting pressure from economic and energy markets. To this end, this paper looks into the operation of a traffic network with distributed, intelligent agents. In particular, it casts the task of operating a traffic network as a distributed, stochastic game in which the agents solve reinforcement-learning problems. Results from computational experiments show that these agents can yield substantial gains with respect to the performance achieved by two other control policies for traffic lights. The paper ends with an outline of future research to deploy machine-learning technology in real-world traffic networks.

90 citations

Patent
31 Aug 2001
TL;DR: In this article, a policy-based network traffic management method is proposed for network traffic, where the first data (A) is received out-of-band of network traffic and the second data (B) is extracted from the network traffic.
Abstract: A method to implement policy-based network traffic management includes receiving data (16) pertaining to a network device at a network traffic manager (10), the first data (A) being received out-of-band of network traffic. Second data (B) is extracted from the network traffic. A network traffic management policy (18) is implemented at the network traffic manager utilizing the first and second data.

89 citations

Proceedings ArticleDOI
19 Mar 2017
TL;DR: A network traffic prediction method based on a deep belief network and a Gaussian model that outperforms three existing methods for wireless mesh backbone network prediction.
Abstract: Wireless mesh network is prevalent for providing a decentralized access for users. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep belief network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then a prediction model is built by learning a deep belief network from the extracted low-pass component. Otherwise, for the rest high-pass component that expresses the gusty and irregular fluctuations of network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high-pass component by the built model. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

89 citations

Patent
22 Apr 2004
TL;DR: In this paper, an automated capturing and characterization of network traffic using feedback is described, which may include capturing network traffic, compiling network traffic data based on the network traffic and automatically providing instructions to control the capturing and the compiling.
Abstract: Automated capturing and characterization of network traffic using feedback is described. A method may include capturing network traffic, compiling network traffic data based on the network traffic, analyzing the network traffic data, and automatically providing instructions based on the analyzing to control the capturing and the compiling. A system on which the method may execute may include one or more chassis or computing devices having one or more network cards. The chassis and/or the computing devices may be connected to one or more networks, and may be connected to one another. A system that implements the method may include collectors, characterization units, and a feedback controller.

89 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202312
202255
20212
20202
20195
201815