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Inferring dynamic origin-destination flows by transport mode using mobile phone dataTransportation research. Part C, Emerging technologies, 2019, 101, pp.254-275. ⟨10.1016/j.trc.2019.02.013⟩
Journal articles
hal-02043639v1
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Using mobile phone data analysis for the estimation of daily urban dynamicsITSC 2017 : 20th International Conference on Intelligent Transportation Systems, Oct 2017, Yokohama, Japan. pp.626 - 632, ⟨10.1109/ITSC.2017.8317956⟩
Conference papers
hal-01745767v1
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Estimating urban mobility with mobile network geolocation data miningNetworking and Internet Architecture [cs.NI]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLL004⟩
Theses
tel-02046122v1
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Combining Bayesian inference and clustering for transport mode detection from sparse and noisy geolocation dataECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases, Sep 2018, Dublin, Ireland. pp.569-584, ⟨10.1007/978-3-030-10997-4_35⟩
Conference papers
hal-01939608v1
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