Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting
Urban transportation systems consist of multiple mobility modes that coexist within the same city and exhibit complex interdependencies, leading to correlated demand dynamics across modes. However, forecasting demand jointly across different modes remains challenging due to substantial heterogeneity in space and the limited availability of historical data for emerging modes. Existing forecasting methods are largely developed for individual mobility modes and implicitly assume compatible spatial structures between source and target systems, which severely restricts their applicability in multi-
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 46%Machine and Deep Learning Advance Traffic Congestion Forecasting in Intelligent Transportation Systems - Bioengineer.org →
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- LinkedLinked via arxiv author · 85%Yixuan Zhao →
“Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting”
- LinkedLinked via arxiv author · 85%Man Luo →
“Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting”