Spatio-temporal prediction on multivariate time series has received tremendous attention for
extensive applications in the real world, where the dynamic unknown spatio-temporal …

The expansion of wind generation and the advance in deep learning have provided
feasibility for multisite wind power prediction motivated by spatiotemporal dependencies …

Accurately understanding the spatial–temporal information of future freight traffic speed in
the metropolitan area is of vital importance to formulate freight-related traffic management …

Research in short-term traffic forecasting has been blooming in recent years due to its
significant implications in traffic management and intelligent transportation systems. The …

Accurate traffic state estimation is vital basis for traffic control and management applications.
However, owing to the multi-modality of traffic state patterns, the estimation methods are …

Recently, transformer-based models have exhibited great performance in multi-horizon time
series forecasting tasks. However, the core module of these models, the self-attention …

The daily long-term traffic prediction is an important urban computing issue, and can give
users a global insight into traffic. Accurate traffic prediction is conducive to rational route …

Congestion is a bane of urban life that affects a large share of the population on a daily
basis. Thus, congestion gets tremendous attention from city stakeholders, residents, and …

Forecasting the forthcoming intersection movement-based traffic volume enables adaptive
traffic control systems to dynamically respond to the fluctuation of traffic demands. In this …

In recent years, significant achievements have been made in time series forecasting using
deep learning methods, particularly the Long Short-Term Memory Network (LSTM) …