arXiv:2507. 00028v2 Announce Type: replace Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns.
arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.
arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.
arXiv:2606. 09086v1 Announce Type: new Abstract: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations.
arXiv:2606. 02287v1 Announce Type: cross Abstract: Urban trajectory generation is a fundamental task for transportation simulation, urban planning, and mobility analytics.
arXiv:2608. 03244v1 Announce Type: new Abstract: Image-goal visual navigation is a fundamental capability for embodied agents.
arXiv:2607. 18353v1 Announce Type: cross Abstract: In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into spatial features, and some models struggle to capture global correlations among spatial regions.
arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.
arXiv:2608. 01039v1 Announce Type: cross Abstract: Trajectory similarity learning is fundamental to efficient trajectory retrieval under complex distance measures.
arXiv:2606. 17978v1 Announce Type: new Abstract: Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection.
arXiv:2608. 08646v1 Announce Type: new Abstract: Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility analysis and personalized location-aware services.
arXiv:2606. 14956v1 Announce Type: new Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.