Traffic forecasting as a canonical task of multivariate time series has been significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied traffic stream, this study, we propose Spatio-Temporal Meta-Graph Learning novel Graph Structure mechanism on data. Specifically, implement idea into Convolutional Recurrent Network (MegaCRN) by pluggin...

Globalization has resulted in increases air transportation demand and passenger traffic. With the traffic, airports face challenges related to infrastructure, services, future development. Air traffic forecasting is essential ensuring appropriate investment airports. In this study, we combined fuzzy theory with support vector regression (SVR) develop a SVR (FSVR) model for international airport...

To achieve greater sustainability of the traffic system, trend accidents in road was analysed. Injuries from are among leading factors suffering people around world. predicted to be third factor contributing human deaths. Road have decreased most countries during last decade because Decade Action for Safety 2011–2020. The main reasons behind reduction improvements construction vehicles and road...

Traffic forecasting is one of the most popular spatio-temporal tasks in field machine learning. A prevalent approach to combine graph convolutional networks and recurrent neural for processing. There has been fierce competition many novel methods have proposed. In this paper, we present method controlled differential equation (STG-NCDE). Neural equations (NCDEs) are a breakthrough concept proce...

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent networks, have been extensively applied in traffic problems to model spatial temporal dependencies. In recent years, graph structures systems as well contextual information, introduced achieved state-of-the-art performance a series pro...

This paper is concerned with long-term (20+years) forecasting of broadband traffic in next-generation networks. Such long-term approach requires going beyond extrapolations of past traffic data while facing high uncertainty in predicting the future developments and facing the fact that, in 20 years, the current network technologies and architectures will be obsolete. Thus, “order of magnitude” ...

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine...