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Data-Driven Transit Network Design From Mobile Phone Trajectories.

, , , , , и . IEEE Trans. Intell. Transp. Syst., 17 (6): 1724-1733 (2016)

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Davide Pinelli

Fabio Sternberg

Fabio Galbusera

Fabio Farinon

Fabio Ruggiero

 

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Location prediction within the mobility data analysis environment DAEDALUS., , , и . MobiQuitous, ICST / ACM, (2008)Temporal analysis of process logs: a case study., , , и . SEBD, стр. 430-437. (2008)Editorial: Human-Interpretable Machine Learning., , и . Frontiers Big Data, (2022)A general methodology for building multiple aspect trajectories., , , и . SAC, стр. 515-517. ACM, (2023)MAT-Builder: a System to Build Semantically Enriched Trajectories., , , и . SEBD, том 3194 из CEUR Workshop Proceedings, стр. 175-182. CEUR-WS.org, (2022)Pattern-Preserving k-Anonymization of Sequences and its Application to Mobil- ity Data Mining., , , и . PiLBA, том 397 из CEUR Workshop Proceedings, CEUR-WS.org, (2008)Uncertainty in urban mobility: Predicting waiting times for shared bicycles and parking lots., , , , и . ITSC, стр. 53-58. IEEE, (2013)Cityride: A Predictive Bike Sharing Journey Advisor., , и . MDM, стр. 306-311. IEEE Computer Society, (2012)Turning Federated Learning Systems Into Covert Channels., , , и . IEEE Access, (2022)Anonymity preserving sequential pattern mining., , , и . Artif. Intell. Law, 22 (2): 141-173 (2014)