arXiv AI By Olaf Yunus Laitinen Imanov

Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility

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arXiv Machine Learning
Aug 31

Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting

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.

By Yixuan Zhao, Man Luo