Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

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

Abstract From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …

The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …

Spatial-temporal forecasting has attracted tremendous attention in a wide range of
applications, and traffic flow prediction is a canonical and typical example. The complex and …

As a core technology of Intelligent Transportation System, traffic flow prediction has a wide
range of applications. The fundamental challenge in traffic flow prediction is to effectively …

Spatial-temporal network data forecasting is of great importance in a huge amount of
applications for traffic management and urban planning. However, the underlying complex …

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …

The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and
the constantly changing nature of many impacting factors. In this paper, we focus on the …