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Research questionHow can urban mobility demand be forecast across modes with mismatched spatial structures and scarce target histories?Mobility modes divide a city into systems with different spatial granularities, so demand patterns learned in one mode may not align directly with another. Emerging modes may also lack enough historical observations to support reliable forecasting independently.
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Machine Learning
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Statistical Machine Learning
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Latest papersRecent research connected to this question, newest first.Learning to Transfer Across Modes: Towards Unified Urban Mobility ForecastingApplies to unified urban mobility demand forecasting that transfers information from data-rich source modes to data-scarce target modes with heterogeneous spatial structures. The source reports experiments on real-world datasets and performance under limited target data, but does not specify particular mobility modes or forecast horizons.research paper · Sep 3, 2026
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