TL;DR
In STTF, a knowledge graph capturing multi-node relationships is built to align trajectories with the road network and a spatial-temporal topology fusion module is designed to integrate temporal information with road network topology explicitly.
Trajectory data may suffer from missing locations due to environmental and equipment constraints, which impact the performance of downstream tasks. Trajectory recovery aims to enhance the utility of mobility data by reconstructing high-quality trajectories. However, existing methods face two significant limitations. Fi...
The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.
To effectively integrate spatial and temporal representations, the proposed Spatio-Temporal Unified Network (STUNet) introduces query-aggregate attention, which simulates the process of tracing upstream and downstream nodes and aggregating their information, thereby capturing complex spatio-temporal dependencies.
A novel spatio-temporally complementary feature propagation framework that leverages the strengths of two distinct data sources: spatially sparse but temporally dense loop detector data, and a spatially complete but temporally sparse macroscopic transportation model is proposed.
Mobility Stream-Structure Synergy (MoSS) is proposed, which derives complementary views from mobility data: a Sequence view that preserves each region's hourly inflow/outflow profile, and a Structure view that captures how regional connectivity emerges, persists, and dissolves over time.
DynaSTar is proposed, a Dynamic Spatio-Temporal Graph Invariant Learning model designed for reliable out-of-time (OOT) traffic prediction under evolving topologies, which employs a dynamic probabilistic graph structure, which is continuously refined through momentum-based updates and differentiable sparse sampling to m...
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