CellTrans: Private Car or Public Transportation? Infer Users' Main Transportation Modes at Urban Scale with Cellular Data

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 to users' main transportation modes and presents solutions for different application scenarios including when there are no labeled users in the studied cities. We evaluate CellTrans on two real-world large-scale cellular datasets covering 3 million users, among which 2,589 users are with labels. We assess our method not only quantitatively with labeled users, but also qualitatively with the whole population. The experiments show that CellTrans infers users' main transportation modes with accuracy over 80% (with a performance gain of 20% compared to state-of-the-art), and CellTrans remains effective when applied at urban scale to the whole population.