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arXiv Machine Learning By Yixuan Zhao, Man Luo

Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting

Read the original on arXiv Machine Learning →

The paper introduces TransMod, a unified framework for forecasting urban mobility demand across multiple transportation modes. It creates a shared zone-level spatial representation to align systems with different spatial granularities, reducing structural mismatch and distributional shift. TransMod then learns transferable spatio-temporal patterns from data-rich source modes and adapts them to data-scarce target modes, improving forecasting performance when target data is limited.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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