Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …

Traffic flow forecasting is the cornerstone of the development of intelligent transportation
systems. Accurate forecasting is conducive to the control and management of urban traffic …

Personalized route recommendation aims to recommend routes based on users' route
preference. The vast amount of GPS trajectories tracking driving behavior has made deep …

Urban crowd density prediction is essential for transport demand management and public
safety monitoring. Existing studies for crowd density prediction only focus on a few transport …

Frost forecast is an important issue in climate research because of its economic impact on
several industries. In this study, we propose GRAST-Frost, a graph neural network (GNN) …

Accurate and timely detection of large events on urban transportation networks enables
informed mobility management. This work tackles the problem of extreme event detection on …

Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
and it also provides valuable reference data for traffic control in advance. Three essential …

The uncertainty of wind power (WP) poses a significant challenge to power systems with a
high percentage of WP. Accurate WP forecasting is an important approach to mitigate this …

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is
very challenging to forecast the traffic flow due to the high non‐linearity, complexity and …

In recent years, traffic forecasting has gradually become a core component of smart cities.
Due to the complex spatial-temporal correlation of traffic data, traffic flow prediction is highly …