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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: This paper provides a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed and shows how this condition can be used to inform traffic sensor placement.
Abstract: The sensor location problem is that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. In this paper, we provide a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed. This condition is not sufficient in general, but we show that for a large class of problem instances, the condition is sufficient. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement.

25 citations

Journal ArticleDOI
TL;DR: An open and flexible software infrastructure that embeds physical hosts in a simulated network that is implemented based on Open Virtual Private Network, modified and customized to bridges traffic between the physical hosts and the simulated network.

25 citations

Journal ArticleDOI
TL;DR: This paper discusses the application of the network traffic simulation model INTEGRATION to a 35-km section of Highway 401 in Toronto, Canada, and results in a correlation coefficient of estimated and observed link flows of 97.23% are presented.
Abstract: This paper discusses the application of the network traffic simulation model INTEGRATION to a 35-km section of Highway 401 in Toronto, Canada. Results for the eastbound direction from 4 a.m. to 12 ...

25 citations

Journal ArticleDOI
TL;DR: A model of radial basis function neural network with improved particle swarm optimization algorithm with optimized parameters is proposed to improve the accuracy of network traffic prediction and optimize the method of parameter and structure setting for a neural network.
Abstract: The neural network data are usually characterized by abruptness, nonlinearity and time variability, and thus, it is difficult to yield accuracy results of network traffic prediction based on a traditional radial basis function neural network that has the shortcomings of slow convergence and easily falling to local optimum. To improve the accuracy of network traffic prediction and optimize the method of parameter and structure setting for a neural network, a model of radial basis function neural network with improved particle swarm optimization algorithm is proposed by referring to the related theories of network traffic and phase space reconstruction. The improved particle swarm optimization algorithm can adjust the inertia weight and the learning factors, and make $$t$$ -distribution mutation of particles’ positions via global extremum to avoid local convergence and thereby improving its global searching capacity; with such an algorithm, the parameters of radial basis function neural network are optimized; then, in order to verify the algorithm’s effectiveness, the radial basis function neural network is trained to become an optimal prediction model, which is adopted for the prediction of two typical chaotic time series and the real network traffic. It is then compared with the traditional radial basis function neural network model and the radial basis function prediction model by improved particle swarm optimization; and the simulations result shows that the application of this model improves the accuracy of network traffic prediction, and demonstrates the algorithm’s feasibility and effectiveness for network traffic prediction.

24 citations

01 Apr 2015
TL;DR: Performance simulation of single tier, multi-tier and D2D based heterogeneous network shows thatheterogeneous network provides significantly higher performance in terms of throughput and signal-to-interference-plus-noise ratio.
Abstract: Next generation wireless networks are expected to provide thousand times higher capacity comparing to existing LTE (Long Term Evolution) networks. Increasing of network capacity can be achieved by combining both spatial and spectral network densification. Influence of spatial network densification on future tremendous capacity growth is very high due to limited spectral resources. Therefore, optimal network planning is an important challenge for future heterogeneous networks with high number of small cells. Network geometry modeling is the significant part of network design and analysis. Multi-tier heterogeneous networks are very complex in terms of topology that requires new advanced approaches to the network planning. In we study the most recent solutions on the stochastic network geometry and analyze their feasibility for different scenarios of heterogeneous network. Studied approached provides good tractability of the mobile network topology and behavior. Poisson point processes combining with Voronoi tessellation provides good approximation of network nodes deployment and coverage areas. We also study feasibility of stochastic models for different buildings environment, including hyper dense skyscrapers environment. Hybrid network model combining Poisson point process with K-means clustering method was developed for D2D (Device-to-Device) heterogeneous network. Proposed model reflects random user behavior and estimate available groups for D2D transmission. Performance simulation of single tier, multi-tier and D2D based heterogeneous network shows that heterogeneous network provides significantly higher performance in terms of throughput and signal-to-interference-plus-noise ratio. Future research directions for network geometry have been outlined in this paper including emerging hot topic of combing the stochastic and deterministic network modelling.

24 citations


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