Synthesising digital twin travellers
- Journal Article
Mobile phone data generated in mobile communication networks has the potential to improve current travel demand models and in general, how we plan for better urban transportation systems. However, due to its high-dimensionality, even if anonymised there still exists the possibility to re-identify the users behind the mobile phone traces. This risk makes its usage outside the telecommunication network incompatible with recent data privacy regulations, hampering its adoption in transportation-related applications. To address this issue, we propose a framework designed only with user-aggregated mobile phone data to synthesise realistic daily individual mobility — Digital Twin Travellers. We explore different strategies built around modified Markov models and an adaption of the Rejection Sampling algorithm to recreate realistic daily schedules and locations. We also define a one-day mobility population score to measure the similarity between the population of generated agents and the real mobile phone user population. Ultimately, we show how with a series of histograms provided by the telecommunication service provider (TSP) it is possible and plausible to disaggregate them into new synthetic and useful individual-level information, building in this way a big data travel demand framework that is designed in accordance with current data privacy regulations. Show more
Journal / seriesTransportation Research Part C: Emerging Technologies
Pages / Article No.
SubjectTravel demand models; Mobile phone data; Generative models; Data privacy
Organisational unit08058 - Singapore-ETH Centre (SEC) / Singapore-ETH Centre (SEC)
03521 - Axhausen, Kay W. / Axhausen, Kay W.
02655 - Netzwerk Stadt und Landschaft D-ARCH
08060 - FCL / FCL
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Is new version of: https://doi.org/10.3929/ethz-b-000442517
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