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Understanding citizens' main transportation modes at urban scale is beneficial to a range of applications, such as urban planning, user profiling, transportation management, and precision marketing. Previous methods on mode inference are mostly focused on utilizing GPS data with high spatiotemporal granularity. However, due to high costs of GPS data collection, the previous work typically is in small scales. In contrast, the cellular data logging interactions between cellphone users and cell towers cover much higher population given the ubiquity of cellphones. Nevertheless, utilizing cellular data introduces new challenges given their low spatiotemporal granularity compared to GPS data. In this paper, we design CellTrans, a novel framework to survey users' main transportation modes (public transportation or private car) at urban scale with cellular data. CellTrans extracts various mobility features that are pertinent …