27 oktober 2020
25 minuten - 25 minutes
When travelling around, people’s mobile phone periodically communicates with the cellular network towers (even there are not call/SMS/internet activities), which leaves lots of “footprints” at these towers. Due to its high coverage of the whole population and relatively low cost, this kind of cellular signaling data, which is available at the mobile telecommunication operators, has attracted strong interests from urban planning, transportation and human geography.
This work aims to develop machine learning methods to detect fine-grained transport modes (including walking, cycling, bus, tram, subway, train, and private cars) using mobile phone cellular signaling data, and evaluate the proposed methods with ground truth data.