Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets
predominantly feature zero values, indicating no incidents, with sporadic high-risk values for …

Spatio-temporal graph learning is a fundamental problem in the Web of Things era, which
enables a plethora of Web applications such as smart cities, human mobility and climate …

Quantifying uncertainty is crucial for robust and reliable predictions. However, existing
spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the …

Spatiotemporal traffic forecasting plays a critical role in intelligent transportation systems,
which empowers diverse urban services. Existing traffic forecasting frameworks usually …

Resilient post-disaster recovery is crucial for the long-term sustainable development of
modern cities, and in this regard, predicting the unusual flows of human mobility when …

For many applications, analyzing the uncertainty of a machine learning model is
indispensable. While research of uncertainty quantification (UQ) techniques is very …

Origin-destination (OD) crowd flow, if more accurately inferred at a fine-grained level, has
the potential to enhance the efficacy of various urban applications. While in practice for …

Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic
forecasting models. These models are typically trained to minimize error on averaged test …

Providing both point estimation and uncertainty quantification for traffic forecasting is crucial
for supporting accurate and reliable services in intelligent transportation systems. However …