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This paper introduces SeoulMMOD, a large-scale multimodal origin-destination (OD) flow benchmark for Seoul, addressing the limitations of existing benchmarks with partial mode coverage. The benchmark comprises three years of hourly OD flows for six transport modes across 25 districts and 426 sub-districts, augmented with diverse contextual signals. Benchmarking 14 models reveals that district-level spatio-temporal models struggle to scale to sub-district forecasting, and joint multi-mode training offers inconsistent accuracy gains.
A single benchmark now enables city-scale, multimodal OD flow forecasting across six modes and sub-district granularity, revealing that scaling spatio-temporal models and naive joint training often fail.
Origin-destination (OD) flow forecasting supports urban mobility decisions by predicting the volume and patterns of human movement across a city. Most existing public OD benchmarks provide only a partial view of citywide mobility, covering a single transport service or at most two modes. With partial mode coverage, observed demand changes are difficult to separate into shifts across transport modes and changes in total travel. We introduce SeoulMMOD, a large-scale public benchmark for citywide multimodal mobility in Seoul. SeoulMMOD provides three years of hourly OD flows estimating total mobility for 6 urban travel modes across 25 districts and 426 sub-districts, together with travel-time, travel-distance, calendar, rainfall, and point-of-interest (POI) signals. Using SeoulMMOD, we benchmark 14 forecasting baselines under a common protocol and analyse the effects of spatial scaling, joint versus mode-separate training, and cross-year generalization. Results show that several spatio-temporal models that work at district level do not scale to sub-district OD forecasting, and that naive joint multi-mode training does not consistently improve accuracy. Together, the dataset and benchmark establish a reproducible testbed for multi-mode, multi-year OD forecasting at the city scale.
