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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 novel nonparametric dynamic time-delay recurrent wavelet neural network model is presented for forecasting traffic flow that incorporates the self-similar, singular, and fractal properties discovered in the traffic flow.
Abstract: Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. In this paper, a novel nonparametric dynamic time-delay recurrent wavelet neural network model is presented for forecasting traffic flow. The model incorporates the self-similar, singular, and fractal properties discovered in the traffic flow. The concept of wavelet frame is introduced and exploited in the model to provide flexibility in the design of wavelets and to add extra features such as adaptable translation parameters desirable in traffic flow forecasting. The statistical autocorrelation function is used for selection of the optimum input dimension of traffic flow time series. The model incorporates both the time of the day and the day of the week of the prediction time. As such, it can be used for long-term traffic flow forecasting in addition to short-term forecasting. The model has been validated using actual freeway traffic flow data. The model can assist traffic engineers and highway agencies to create effective traffic management plans for alleviating freeway congestions.

344 citations

Journal ArticleDOI
TL;DR: Simulation results show that the hybridmultiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases.
Abstract: Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner

334 citations

Proceedings ArticleDOI
25 Aug 2003
TL;DR: This work introduces a new network simulation environment, developed by the research group, called the Georgia Tech Network Simulator (GTNetS), designed specifically to allow much larger-scale simulations than can easily be created by existing network simulation tools.
Abstract: We introduce a new network simulation environment, developed by our research group, called the Georgia Tech Network Simulator (GTNetS). Our simulator is designed specifically to allow much larger-scale simulations than can easily be created by existing network simulation tools. The design of the simulator very closely matches the design of real network protocol stacks and hardware. Thus, anyone with a good understanding of networking in general can easily understand how the simulations are constructed. Further, our simulator is implemented completely in object-oriented C++, which leads to easy extension by users to experiment with new or modified behavior of existing simulation models. Our tool is designed from the beginning with scalability in mind, including the support for distributed simulations on a network of workstations as part of the basic design.We give an overview of the features of GTNetS, and present some preliminary scalability results we have obtained by running GTNetS on a computing cluster at the Pittsburgh Supercomputer Center.

320 citations

Proceedings Article
01 Jan 2009
TL;DR: This paper proposes a novel method for thwarting statistical traffic analysis algorithms by optimally morphing one class of traffic to look like another class, and shows how to optimally modify packets in real-time to reduce the accuracy of a variety of traffic classifiers while incurring much less overhead than padding.
Abstract: Recent work has shown that properties of network traffic that remain observable after encryption, namely packet sizes and timing, can reveal surprising information about the traffic’s contents (e.g., the language of a VoIP call [29], passwords in secure shell logins [20], or even web browsing habits [21, 14]). While there are some legitimate uses for encrypted traffic analysis, these techniques also raise important questions about the privacy of encrypted communications. A common tactic for mitigating such threats is to pad packets to uniform sizes or to send packets at fixed timing intervals; however, this approach is often inefficient. In this paper, we propose a novel method for thwarting statistical traffic analysis algorithms by optimally morphing one class of traffic to look like another class. Through the use of convex optimization techniques, we show how to optimally modify packets in real-time to reduce the accuracy of a variety of traffic classifiers while incurring much less overhead than padding. Our evaluation of this technique against two published traffic classifiers for VoIP [29] and web traffic [14] shows that morphing works well on a wide range of network data—in some cases, simultaneously providing better privacy and lower overhead than naive

318 citations

Proceedings ArticleDOI
25 Aug 2003
TL;DR: A new method of traffic characterization that automatically groups traffic into minimal clusters of conspicuous consumption that can be used to automatically classify new traffic patterns, such as network worms or peer-to-peer applications, without knowing the structure of such traffic a priori.
Abstract: The Internet service model emphasizes flexibility -- any node can send any type of traffic at any time. While this design has allowed new applications and usage models to flourish, it also makes the job of network management significantly more challenging. This paper describes a new method of traffic characterization that automatically groups traffic into minimal clusters of conspicuous consumption. Rather than providing a static analysis specialized to capture flows, applications, or network-to-network traffic matrices, our approach dynamically produces hybrid traffic definitions that match the underlying usage. For example, rather than report five hundred small flows, or the amount of TCP traffic to port 80, or the "top ten hosts", our method might reveal that a certain percent of traffic was used by TCP connections between AOL clients and a particular group of Web servers. Similarly, our technique can be used to automatically classify new traffic patterns, such as network worms or peer-to-peer applications, without knowing the structure of such traffic a priori. We describe a series of algorithms for constructing these traffic clusters and minimizing their representation. In addition, we describe the design of our prototype system, AutoFocus and our experiences using it to discover the dominant and unusual modes of usage on several different production networks.

314 citations


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