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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 9

Position Papers of the 2016 Federated Conference on Computer Science and Information Systems

QueuePredict – accurate prediction of queue length in public service offices on the basis of Open Urban Data APIs

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DOI: http://dx.doi.org/10.15439/2016F503

Citation: Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 9, pages 161–164 ()

Full text

Abstract. This paper presents the methods to predict the number of people waiting in queues in Districts Offices of the City of Warsaw. On the basis of information from real-time queues length exposed as a part of Open City Data portal we can predict number of people in given time frame, which can be then used to further estimate predicted waiting time. These information are important and useful for number of people that visit district offices every day. The methods presented in the paper can be used to build a new value-added smart city services.

References

  1. City of Warsaw Open Urban Data platform, https://api.um.warszawa.pl/
  2. Qmatic company webpage, http://www.qmatic.com/
  3. Customer Experience Management, Qmatic white paper, Nov 2015, available online at http://lp.qmatic.com/the-customer-experience-orchestrated
  4. Białołęka District Office, visit registration webpage, available at http://bialoleka.waw.pl/strona-451-internetowa_rejestacja_wizyt_w_urzedzie.html
  5. Białołęka District Office, queue monitoring webpage, available at http://bialoleka.waw.pl/strona-450-sprawdz_kolejke.html
  6. Apps4Warsaw project page, available at http://www.apps4warsaw.org/
  7. Bihapi contest webpage, available at http://bihapi.pl/
  8. ‘Staczkolejkowy’, application description at Apps4Warsaw page, available online at https://konkurs.danepowarszawsku.pl/pl/projekt/stacz-kolejkowy
  9. ‘Caffe kolejka’, application description at Apps4Warsaw page, available online at https://konkurs.danepowarszawsku.pl/pl/projekt/caffe-kolejka
  10. ‘Pan tu nie stał’, application description at Apps4Warsaw page, available online at https://konkurs.danepowarszawsku.pl/pl/projekt/pan-tu-nie-stal
  11. ‘lessQstress’, application description at Apps4Warsaw page, available online at https://konkurs.danepowarszawsku.pl/pl/projekt/lessqstress
  12. ‘shortQ’, application description at Apps4Warsaw page, available online at https://konkurs.danepowarszawsku.pl/pl/projekt/shortq
  13. L. Breiman, “Random Forests,” Machine Learning, 45, pp. 5-32, Kluwer Academic Publishers, 2001.