Efficient uplink scheduling in Ethernet passive optical networks (EPONs) is very important for maximizing the network capacity while maintaining the required quality of service (QoS). Several variants of dynamic bandwidth resource allocation have been proposed in recent research literature. However, the available techniques do not fully exploit the elastic properties of the user traffic. In thi...

Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-...

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction for unmanned ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach that incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions...

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considerin...

overtaking a slow lead vehicle is a complex maneuver because of the variety of overtaking conditions and driver behavior. in this study, two novel prediction models for overtaking behavior are proposed. these models are derived based on multi-input multi-output adaptive neuro-fuzzy inference system (manfis). they are validated at microscopic level and are able to simulate and predict the future...

Monitoring and mining real-time network data streams is crucial for managing and operating data networks. The information that network operators desire to extract from the network traffic is of different size, granularity and accuracy depending on the measurement task (e.g., relevant data for capacity planning and intrusion detection are very different). To satisfy these different demands, a ne...

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme ...

Received Sep 15, 2017 Revised Nov 10, 2017 Accepted Nov 20, 2017 Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic pre...

As an important component of intelligent transportation-management systems, accurate traffic-parameter prediction can help traffic-management departments to conduct effective traffic management. Due the nonlinearity, complexity, and dynamism highway-traffic data, traffic-flow is still a challenging issue. Currently, most spatial–temporal traffic-flow-prediction models adopt fixed-structure time...

This work introduces the wavelet transform as an important element for ATM traffic prediction. Two methods are proposed. The first method proposes data fittings by the fGn model of parameter H, adequate for long dependence stationary processes. The estimated H is accomplished by a method based on Wavelet analysis. A small order Wiener filter is projected to implement the prediction and the fina...