As many location-based applications provide services for users based on traffic conditions,
an accurate traffic prediction model is very significant, particularly for long-term traffic …

Recently, traffic prediction based on deep learning methods has attracted much attention.
However, there still exist two major challenges, namely, dynamic spatio-temporal …

Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic
flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified …

Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …

Research in deep learning models to forecast traffic intensities has gained great attention in
recent years due to their capability to capture the complex spatio-temporal relationships …

Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in
traffic management, urban computing, public safety, and so on. Recently, the availability of …

Exploiting deep spatial–temporal features for traffic prediction has become growing
widespread. Accurate traffic prediction is still challenging due to the complex spatial …

To capture spatial relationships and temporal dynamics in traffic data, spatio-temporal
models for traffic forecasting have drawn significant attention in recent years. Most of the …

Traffic flow prediction is crucial for intelligent transportation system, such as traffic
management, congestion alleviation and public risk assessment. Recently, attention …

Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …