MIP uses memory-bank prompts and variance-based invariant training to improve urban flow prediction under distribution shifts on METR-LA and NYCBike1.
An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city,
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Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
MIP uses memory-bank prompts and variance-based invariant training to improve urban flow prediction under distribution shifts on METR-LA and NYCBike1.