@inproceedings{schuster:hal-02510141,
  TITLE = {{Predicting Parking Demand with Open Data}},
  AUTHOR = {Schuster, Thomas and Volz, Raphael},
  URL = {https://inria.hal.science/hal-02510141},
  NOTE = {Part 3: Open Science and Open Data},
  BOOKTITLE = {{18th Conference on e-Business, e-Services and e-Society (I3E)}},
  ADDRESS = {Trondheim, Norway},
  EDITOR = {Ilias O. Pappas and Patrick Mikalef and Yogesh K. Dwivedi and Letizia Jaccheri and John Krogstie and Matti M{\"a}ntym{\"a}ki},
  PUBLISHER = {{Springer International Publishing}},
  SERIES = {Digital Transformation for a Sustainable Society in the 21st Century},
  VOLUME = {LNCS-11701},
  PAGES = {218-229},
  YEAR = {2019},
  MONTH = Sep,
  DOI = {10.1007/978-3-030-29374-1\_18},
  KEYWORDS = {Parking prediction ; Machine learning ; Data mining ; Smart cities},
  PDF = {https://inria.hal.science/hal-02510141/file/I3E2019_paper_77.pdf},
  HAL_ID = {hal-02510141},
  HAL_VERSION = {v1},
}