AI Research
LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling
Bridging the gap in spatial intelligence with inductive location embeddings for smarter urban mobility modeling.
Location representations provide mobility models with fundamental information about the spatial position, functional characteristics, and relationships of places. However, existing embeddings are often dependent on mobility observations, unable to represent unseen locations, and ...
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ArXiv CS.LG
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ArXiv CS.LG
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LE4Mob addresses a critical bottleneck in urban computing by enabling models to interpret spatial relationships for locations without prior mobility data. This advancement paves the way for more robust, inductive AI systems that can seamlessly generalize across diverse geographic regions.
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