Information Technology & Tourism

, Volume 17, Issue 3, pp 275–314 | Cite as

Planning the trip itinerary for tourist groups

Original Research

Abstract

Sightseeing trips are often done in groups, where tourists enjoy their trip in company with their relatives or friends. Therefore, in this paper, in order to model the case of trips for tourist groups, we introduce a new problem, as an extension of the existing problem in the literature that is used for planning the trip of a single tourist. The new problem extends the existing problem with two additional concepts. The first is the consideration of multiple tourists, where their individual preferences about points of interests are taken into account, and the second is the introduction of the concept of mutual social relationship between the different tourists. For the actual single tourist trip problem, we use an algorithm that obtains comparable results with the state of the art algorithms, whereas for the group trip problem, since no solution has been published before, we design a new algorithm based on tabu search metaheuristic that uses two new unique operators for exploring the search space. As a result, this paper proposes an anytime algorithm that in average takes about 20 s to obtain better personalized itineraries for tourist groups than when scheduling the whole group together.

Keywords

Trip itinerary Tourist groups Planning Tabu search 

References

  1. Brilhante IR, Macedo JA, Nardini FM, Perego R, Renso C (2015) On planning sightseeing tours with tripbuilder.Inf Process Manag 51(2):1–15CrossRefGoogle Scholar
  2. Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Theory Methods 3(1):1–27CrossRefGoogle Scholar
  3. Chao I, Golden BL, Wasil EA et al (1996) The team orienteering problem. Eur J Oper Res 88(3):464–474CrossRefGoogle Scholar
  4. Cordeau JF, Gendreau M, Laporte G (1997) A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30(2):105–119CrossRefGoogle Scholar
  5. Delic A, Neidhardt J, Nguyen TN, Ricci F, Rook L, Werthner H, Zanker M (2016) Observing group decision making processes. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 147–150Google Scholar
  6. Fomin FV, Lingas A (2002) Approximation algorithms for time-dependent orienteering. Inf Process Lett 83(2):57–62CrossRefGoogle Scholar
  7. Garcia A, Vansteenwegen P, Souffriau W, Arbelaitz O, Linaza M (2009) Solving multi constrained team orienteering problems to generate tourist routes. (status: published) Google Scholar
  8. Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N (2017) Scenic route planning for tourists. Person Ubiquit Comput (in press) Google Scholar
  9. Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N, Zaroliagis C (2015) The eCOMPASS multimodal tourist tour planner. Expert Syst Appl 42(21):7303–7316CrossRefGoogle Scholar
  10. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014a) Mobile recommender systems in tourism.J Netw Comput Appl 39:319–333CrossRefGoogle Scholar
  11. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014b) A survey on algorithmic approaches for solving tourist trip design problems. J Heurist 20(3):291–328CrossRefGoogle Scholar
  12. Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G, Tasoulas Y (2013) Cluster-based heuristics for the team orienteering problem with time windows. In: Experimental algorithms. Springer, Berlin, pp 390–401Google Scholar
  13. Glover F (1989a) Tabu search—part 1. ORSA J Comput 1(3):190–206CrossRefGoogle Scholar
  14. Glover F (1989b) Tabu search—part 2. ORSA J Comput 2(1):4–32CrossRefGoogle Scholar
  15. Glover F, McMillan C (1986) The general employee scheduling problem. an integration of ms and ai. Comput Oper Res 13(5):563–573CrossRefGoogle Scholar
  16. Kurata Y, Hara T (2013) Ct-planner4: toward a more user-friendly interactive day-tour planner. In: Information and communication technologies in tourism 2014. Springer, Berlin, pp 73–86Google Scholar
  17. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36(2):451–461Google Scholar
  18. Lloyd S (1957) Least squares quantization in pcm. In: Unpublished Bell Lab. Tech. Note. portions presented at the Institute of Mathematical Statistics Meeting Atlantic CityGoogle Scholar
  19. Lloyd S (1982) Least squares quantization in pcm. IEEE Trans 28(2):129–137CrossRefGoogle Scholar
  20. Masthoff J (2015) Group recommender systems: aggregation, satisfaction and group attributes. In: Recommender systems handbook. Springer, Berlin, pp 743–776Google Scholar
  21. Ramesh R, Brown KM (1991) An efficient four-phase heuristic for the generalized orienteering problem. Comput Oper Res 18(2):151–165CrossRefGoogle Scholar
  22. Schaller R (2011) Planning and navigational assistance for distributed events. In: Proceedings of the 2nd workshop on context aware intelligent assistance, BerlinGoogle Scholar
  23. Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265CrossRefGoogle Scholar
  24. Souffriau W, Vansteenwegen P (2010) Tourist trip planning functionalities: state-of-the-art and future. In: Current trends in web engineering. Springer, Berlin, pp 474–485Google Scholar
  25. Souffriau W, Vansteenwegen P, Vanden Berghe G, Van Oudheusden D (2013) The multiconstraint team orienteering problem with multiple time windows. Transp Sci 47(1):53–63Google Scholar
  26. Souffriau W, Vansteenwegen P, Vertommen J, Berghe GV, Oudheusden DV (2008) A personalized tourist trip design algorithm for mobile tourist guides. Appl Artif Intell 22(10):964–985CrossRefGoogle Scholar
  27. Sylejmani K (2013) Optimizing trip itinerary for tourist groups. Ph.D. thesis, Vienna University of Technology, Faculty of Informatics, AustriaGoogle Scholar
  28. Sylejmani K, Dorn J, Musliu N (2012) A tabu search approach for multi constrained team orienteering problem and its application in touristic trip planning. In: 2012 12th international conference on hybrid intelligent systems (HIS). IEEE, pp 300–305Google Scholar
  29. Tsiligirides T (1984) Heuristic methods applied to orienteering. J Oper Res Soc 35:797–809Google Scholar
  30. Tumas G, Ricci F (2009) Personalized mobile city transport advisory system. Inf Commun Technol Tour 2009:173–183Google Scholar
  31. Vansteenwegen P (2008) Planning in tourism and public transportation attraction selection by means of a personalised electronic tourist guide and train transfer scheduling. PhD thesis, Katholieke Universiteit Leuven, Centre for Industrial Management, BelgiumGoogle Scholar
  32. Vansteenwegen P, Souffriau W, Berghe GV, Oudheusden DV (2011a) The city trip planner: an expert system for tourists. Expert Syst Appl 38(6):6540–6546CrossRefGoogle Scholar
  33. Vansteenwegen P, Souffriau W, Oudheusden DV (2011b) The orienteering problem: a survey. Eur J Oper Res 209(1):1–10CrossRefGoogle Scholar
  34. Vansteenwegen P, Souffriau W, Vanden Berghe G, Van Oudheusden D (2009) Iterated local search for the team orienteering problem with time windows. Comput Oper Res 36(12):3281–3290CrossRefGoogle Scholar
  35. Zenker B, Ludwig B (2009) Rose: assisting pedestrians to find preferred events and comfortable public transport connections. In: Proceedings of the 6th international conference on mobile technology, application and systems. ACM, p 16Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Faculty of Electrical and Computer EngineeringUniversity of PrishtinaPrishtinaRepublic of Kosovo
  2. 2.E-Commerce Group, Institute of Software Technology and Interactive Systems (188), Faculty of InformaticsVienna University of TechnologyViennaAustria
  3. 3.Database and Artificial Intelligence Group, Faculty of InformaticsVienna University of TechnologyViennaAustria

Personalised recommendations