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

Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

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 …

Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …

ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …

Deep learning model (primarily convolutional networks and LSTM) for time series
classification has been studied broadly by the community with the wide applications in …

Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS).
Existing methods focus on spatial-temporal dependency modeling, while two intrinsic …

Dealing with missing values and incomplete time series is a labor-intensive, tedious,
inevitable task when handling data coming from real-world applications. Effective spatio …