MobilityNet: Towards a Public Dataset for Multi-modal Mobility Research

Kalyanaraman Shankari, Jonathan Fuerst, Mauricio Fadel Argerich, Eleftherios Avramidis, Jesse Zhang

In: Proceedings of the ICLR 2020 Workshop on Tackling Climate Change with Machine Learning. International Conference on Learning Representations (ICLR-2020) befindet sich ICLR2020 April 26-30 Climate Change AI 5/2020.


Influencing transportation demand can significantly reduce CO2 emissions. Individual user mobility models are key to influencing demand at the personal and structural levels. Constructing such models is a challenging task that depends on a number of interdependent steps. Progress on this task is hamstrung by the lack of high quality public datasets. We introduce MobilityNet: the first step towards a common ground for multi-modal mobility research. MobilityNet solves the holistic evaluation, privacy preservation and fine grained ground truth problems through the use of artificial trips, control phones, and repeated travel. It currently includes 1080 hours of data from both Android and iOS, representing 16 different travel contexts and 4 different sensing configurations.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence