Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Abstract Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a
new breed of unprecedented applications in different domains. This technology's ongoing …

AH igh-D imensional and I ncomplete (HDI) tensor is frequently encountered in a big data-
related application concerning the complex dynamic interactions among numerous entities …

Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

The missing data problem is inevitable when collecting traffic data from intelligent
transportation systems. Previous studies have shown the advantages of tensor completion …

Deep learning approaches have reached a celebrity status in artificial intelligence field, its
success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By …

Accurate estimation of the traffic state can help to address the issue of urban traffic
congestion, providing guiding advices for people's travel and traffic regulation. In this paper …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …