Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Abstract Intelligent Transportation Systems (ITS) have attracted an increasing amount of
attention in recent years. Thanks to the fast development of vehicular computing hardware …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Traffic forecasting has attracted considerable attention due to its importance in proactive
urban traffic control and management. Scholars and engineers have exerted considerable …

Accurate traffic forecasting is of vital importance for the management and decision in
intelligent transportation systems. Indeed, it is a nontrivial endeavor to predict future traffic …

In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …

Traffic prediction is an important component of the intelligent transportation system. Existing
deep learning methods encode temporal information and spatial information separately or …

In this article, we present a deep learning method to predict the temporal evolution of
dissipative dynamic systems. We propose using both geometric and thermodynamic …

This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for
traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined …