This paper presents NeuTM, a framework for network Traffic Matrix (TM) prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM RNNs). TM prediction is defined as the problem of estimating future network traffic matrix from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LS...

This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle trajectories and incorporates into road traffic prediction. The trajectory transition patterns are studied to explicitly model spatial demand via graph propagation along network; an attention mechanism is designed learn...

With service types and requirements of broadband satellite internet continuously increasing, improving QoS (Quality of Service) of satellite internet has attracted extensive attention. To reduce the impact of selfsimilarity caused by various of service traffic sources converging on satellite communication system, this paper establishes a novel model from the perspective of self-similar traffic ...

In a wireless network environment accurate and timely estimation or prediction of network traffic has gained much importance in the recent past. The network applications use traffic prediction results to maintain its performance by adopting its behaviors. Network Service provider will use the prediction values in ensuring the better Quality of Service(QoS) to the network users by admission cont...

The recent economic growth in developing countries like India has resulted in an intense increase of vehicle ownership and use, as witnessed by severe traffic congestion and bottlenecks during peak hours in most of the metropolitan cities. Intelligent Transportation Systems (ITS) aim to reduce traffic congestion by adopting various strategies such as providing pre-trip and en-route traffic info...

Provably safe motion planning for automated road vehicles must ensure that planned motions do not result in a collision with other traffic participants. This is major challenge autonomous driving, since the future behavior of participants known and are often hidden due to occlusions. In this work, we propose formal set-based prediction contains all acceptable behaviors both detected potentially...

This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow predict...

Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management. In this paper, Least Mean Kurtosis (LMK), which use...