In order to improve the performance of network traffic prediction model, a novel network traffic prediction model is proposed in this paper which embedding dimension and time delay of network traffic time series are jointly optimized by genetic algorithm. The optimail embedding dimension and time delay are used to establish the one-step and multi-step based on RBF neural network, finally, the s...

Traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is possible by analyzing the real-time transportation data from correlative roads and vehicles. This...

The network of application service is becoming more and more increasingly complex, with the development of network communication technology, which puts forward higher requirements on network behavior characteristics, the network management and traffic control, therefore, network traffic analysis and prediction is more and more important significance. This paper presents a novel network traffic ...

Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the lo...

Network traffic prediction is important to network planning, performance evaluation and network management directly. A variety of machine learning models such as artificial neural networks (ANN) and support vector machine (SVM) have been applied in traffic prediction. In this paper, a novel network traffic one-step-ahead prediction technique is proposed based on a state-ofthe-art learning model...

Highways and in particular their pavements are the fundamental components of the road network. They require continuous maintenance since they deteriorate due to changing traffic and environmental conditions. Monitoring methods and efficient pavement management systems are needed for optimizing maintenance operations. Pavement performance prediction models are useful tools for determining the op...

Short-term traffic flow is difficult to predict because of high uncertainty. This paper proposes a short-term traffic forecast algorithm based on cloud similarity. By taking advantage of quantitative and qualitative cloud model mutual conversion function and traffic flow predictability, the historical traffic data can be processed with cloud transformation. Set the current traffic cloud as a st...

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations ...

In this research the congestion prediction in nodal packet switches such as the routers is concerned. We introduce and verify a novel scheme for the prediction based on the reinforcement learning. It is a dynamic scheme and non sensitive to short term independence of the traffic flow. We assume a self similar behavior for the traffic and its long term correlation is used as the prediction basis...

This work extends the notion of the widely mentioned and used fractional Brownian traffic model in the literature. Extensive experimental investigations indicate that the proposed traffic model, named extended fractional Brownian traffic, can capture not only the self-similar properties, but also the inherent multifractal characteristics of those traffic flows found in modern communication netw...