Tourismo: A User-Preference Tourist Trip Search Engine

  • Gregor Jossé
  • Klaus Arthur Schmid
  • Andreas Züfle
  • Georgios Skoumas
  • Matthias Schubert
  • Dieter Pfoser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

Abstract

In this demonstration we re-visit the problem of finding an optimal route from location A to B. Currently, navigation systems compute shortest, fastest, most economic routes or any combination thereof. More often than not users want to consider “soft” qualitative metrics such as popularity, scenic value, and general appeal of a route. Routing algorithms have not (yet) been able to appreciate, measure, and evaluate such qualitative measures. Given the emergence of user-generated content, data exists that records user preference. This work exploits user-generated data, including image data, text data and trajectory data, to estimate the attractiveness of parts of the spatial network in relation to a particular user. We enrich the spatial network dataset by quantitative scores reflecting qualitative attractiveness. These scores are derived from a user-specific self-assessment (“On vacation I am interested in: family entertainment, cultural activities, exotic food”) and the selection of a respective subset of existing POIs. Using the enriched network, our demonstrator allows to perform a bicriterion optimal path search, which optimizes both travel time as well as the attractiveness of the route. Users will be able to choose from a whole skyline of alternative routes based on their preference. A chosen route will also be illustrated using user-generated data, such as images, textual narrative, and trajectories, i.e., data that showcase attractiveness and hopefully lead to a perfect trip.

References

  1. 1.
    Andersen, O., Jensen, C.S., Torp, K., Yang, B.: Ecotour: reducing the environmental footprint of vehicles using eco-routes. In: MDM, pp. 338–340 (2013)Google Scholar
  2. 2.
    Graf, F., Kriegel, H.-P., Renz, M., Schubert, M.: MARiO: multi-attribute routing in open street map. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 486–490. Springer, Heidelberg (2011)Google Scholar
  3. 3.
    Shekelyan, M., Jossé, G., Schubert, M.: Linear path skylines in multicriteria networks. In: ICDE 2015, pp. 459–470 (2015)Google Scholar
  4. 4.
    Shekelyan, M., Jossé, G., Schubert, M.: Paretoprep: efficient lower bounds for path skylines and fast path computation. In: SSTD 2015 (2015)Google Scholar
  5. 5.
    Jossé, G., Franzke, M., Skoumas, G., Züfle, A., Nascimento, M.A., Renz, M.: A framework for computation of popular paths from crowdsourced data. In: ICDE, pp. 1428–1431 (2015)Google Scholar
  6. 6.
    Sacharidis, D., Bouros, P.: Routing directions: keeping it fast and simple. In: ACM SIGSPATIAL GIS, pp. 164–173 (2013)Google Scholar
  7. 7.
    Westphal, M., Renz, J.: Evaluating and minimizing ambiguities in qualitative route instructions. In: ACM SIGSPATIAL GIS, pp. 171–180 (2011)Google Scholar
  8. 8.
    Garcia, A., Arbelaitz, O., Linaza, M.T., Vansteenwegen, P., Souffriau, W.: Personalized tourist route generation. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 486–497. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  9. 9.
    Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G.: A survey on algorithmic approaches for solving tourist trip design problems. J. Heuristics 20, 291–328 (2014)CrossRefGoogle Scholar
  10. 10.
    Kanza, Y., Safra, E., Sagiv, Y., Doytsher, Y.: Heuristic algorithms for route-search queries over geographical data. In: ACM SIGSPATIAL GIS, p. 11 (2008)Google Scholar
  11. 11.
    Chen, H., Ku, W.S., Sun, M.T., Zimmermann, R.: The partial sequenced route query with traveling rules in road networks. Geoinformatica 15, 541–569 (2011)CrossRefGoogle Scholar
  12. 12.
    Quercia, D., Schifanella, R., Aiello, L.M.: The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In: CoRR (abs/1407.1031) (2014)Google Scholar
  13. 13.
    Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: ICDE, pp. 900–911 (2011)Google Scholar
  14. 14.
    Newson, P., Krumm, J.: Hidden markov map matching through noise and sparseness. In: ACM SIGSPATIAL GIS, pp. 336–343 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gregor Jossé
    • 1
  • Klaus Arthur Schmid
    • 1
  • Andreas Züfle
    • 1
  • Georgios Skoumas
    • 2
  • Matthias Schubert
    • 1
  • Dieter Pfoser
    • 3
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany
  2. 2.National Technical University of AthensAthensGreece
  3. 3.George Mason UniversityFairfaxUSA

Personalised recommendations