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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: The urban road traffic weighted network model is redefined with considering the functional properties of urban road network and the traffic efficiency concept of the road section in the urban road Traffic network is presented.

50 citations

Proceedings ArticleDOI
14 Dec 2015
TL;DR: To recover the missing entries in tensors of traffic data, a novel spatio-temporal tensor completion method has been proposed that can significantly reduce the missing traffic data recovery errors and achieve satisfactory completion accuracy comparing with the state-of-the-art completion methods.
Abstract: Network traffic data consists of Traffic Matrix (TM), which represents the volumes of traffic between Origin and Destination (OD) pairs in the network. It is a key input parameter of network engineering tasks. However, direct measurement of the OD pairs traffic is usually not feasible. Even good traffic measurement systems can suffer from errors, missing data. So obtaining the ODs traffic precisely is a challenge. Existing completion methods often perform poorly for network traffic estimation. Their recovery accuracy tends to be significantly worse when the data loss rate is high. Taking into account network traffic lower-dimensional latent structure and traffic hidden characteristic, a tensor (multi-way array) is introduced to model a time series of pure spatial traffic matrices in this paper. To recover the missing entries in tensors of traffic data, a novel spatio-temporal tensor completion method has been proposed. This approach not only takes advantage of tensor decomposition and its lower-dimensional representation, but also well takes into account traffic spatio-temporal properties. The extensive experiments with the real-world traffic trace data show that the proposed method can significantly reduce the missing traffic data recovery errors and achieve satisfactory completion accuracy comparing with the state-of-the-art completion methods.

49 citations

Proceedings ArticleDOI
01 Oct 2007
TL;DR: An extensive analysis of the gains of network coding as compared to traditional transmission strategies in a single-hop setting is provided, and it is shown that the gains are significant in general and can be considerably large in some cases.
Abstract: We study the scaling law governing the delay gains of network coding as compared to traditional transmission strategies in unreliable wireless networks. We distinguish between two types of traffic, namely elastic and inelastic, where the elasticity of a flow is based on the delay constraints associated with it. This novel formulation is useful in that it allows for the modeling of real-time traffic more accurately. Considering the limited availability of feedback in such systems, we focus on strategies with minimal acknowledgement requirements. Under both traffic types, we provide an extensive analysis of the gains of network coding as compared to traditional transmission strategies in a single-hop setting, and show that the gains are significant in general and can be considerably large in some cases. We further provide a method for realizing these gains in multi-hop networks with general topologies using the analysis of the single hop scenario.

49 citations

Journal ArticleDOI
TL;DR: The spatiotemporal dependency of traffic flow is investigated using cross-correlation analysis and its implications in terms of traffic forecastability and real-time data effectiveness can help to understand traffic flow, and hence improve the performance of forecasting models.
Abstract: Short-term traffic forecasting is playing an increasing role in modern transport management. Although many short-term traffic forecasting methods have been explored, the spatiotemporal dependency of traffic flow, an important characteristic of traffic dynamics that can benefit the forecasting of traffic changes, is often neglected in short-term traffic forecasting. This paper first investigates the spatiotemporal dependency of traffic flow using cross-correlation analysis and then discusses its implications in terms of traffic forecastability and real-time data effectiveness. This can help us to understand traffic flow, and hence improve the performance of forecasting models.

48 citations


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