arXiv Machine Learning By Taeyoung Yu, Seonbin Jo, Jiwon Kim, Junyoung Byun

SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark

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SeoulMMOD is a new large‑scale public benchmark that provides three years of hourly origin‑destination flow data for six urban travel modes across 25 districts and 426 sub‑districts in Seoul, along with travel‑time, travel‑distance, calendar, rainfall, and point‑of‑interest signals. The dataset enables city‑wide multimodal mobility analysis and supports the evaluation of 14 forecasting baselines under a common protocol. The benchmark reveals that models effective at the district level may not scale to sub‑district forecasting and that naive joint multi‑mode training does not always improve accuracy.

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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