Skip to main content
SpringerLink
Log in

Planning the trip itinerary for tourist groups

  • Original Research
  • Published:
Information Technology & Tourism Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Brilhante IR, Macedo JA, Nardini FM, Perego R, Renso C (2015) On planning sightseeing tours with tripbuilder.Inf Process Manag 51(2):1–15

    Article  Google Scholar 

  • Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat Theory Methods 3(1):1–27

    Article  Google Scholar 

  • Chao I, Golden BL, Wasil EA et al (1996) The team orienteering problem. Eur J Oper Res 88(3):464–474

    Article  Google Scholar 

  • Cordeau JF, Gendreau M, Laporte G (1997) A tabu search heuristic for periodic and multi-depot vehicle routing problems. Networks 30(2):105–119

    Article  Google Scholar 

  • 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 147–150

  • Fomin FV, Lingas A (2002) Approximation algorithms for time-dependent orienteering. Inf Process Lett 83(2):57–62

    Article  Google Scholar 

  • Garcia A, Vansteenwegen P, Souffriau W, Arbelaitz O, Linaza M (2009) Solving multi constrained team orienteering problems to generate tourist routes. (status: published)

  • Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N (2017) Scenic route planning for tourists. Person Ubiquit Comput (in press)

  • Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N, Zaroliagis C (2015) The eCOMPASS multimodal tourist tour planner. Expert Syst Appl 42(21):7303–7316

    Article  Google Scholar 

  • Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014a) Mobile recommender systems in tourism.J Netw Comput Appl 39:319–333

    Article  Google Scholar 

  • Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014b) A survey on algorithmic approaches for solving tourist trip design problems. J Heurist 20(3):291–328

    Article  Google Scholar 

  • 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 390–401

  • Glover F (1989a) Tabu search—part 1. ORSA J Comput 1(3):190–206

    Article  Google Scholar 

  • Glover F (1989b) Tabu search—part 2. ORSA J Comput 2(1):4–32

    Article  Google Scholar 

  • Glover F, McMillan C (1986) The general employee scheduling problem. an integration of ms and ai. Comput Oper Res 13(5):563–573

    Article  Google Scholar 

  • 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 73–86

  • Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36(2):451–461

  • Lloyd S (1957) Least squares quantization in pcm. In: Unpublished Bell Lab. Tech. Note. portions presented at the Institute of Mathematical Statistics Meeting Atlantic City

  • Lloyd S (1982) Least squares quantization in pcm. IEEE Trans 28(2):129–137

    Article  Google Scholar 

  • Masthoff J (2015) Group recommender systems: aggregation, satisfaction and group attributes. In: Recommender systems handbook. Springer, Berlin, pp 743–776

  • Ramesh R, Brown KM (1991) An efficient four-phase heuristic for the generalized orienteering problem. Comput Oper Res 18(2):151–165

    Article  Google Scholar 

  • Schaller R (2011) Planning and navigational assistance for distributed events. In: Proceedings of the 2nd workshop on context aware intelligent assistance, Berlin

  • Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35(2):254–265

    Article  Google Scholar 

  • Souffriau W, Vansteenwegen P (2010) Tourist trip planning functionalities: state-of-the-art and future. In: Current trends in web engineering. Springer, Berlin, pp 474–485

  • Souffriau W, Vansteenwegen P, Vanden Berghe G, Van Oudheusden D (2013) The multiconstraint team orienteering problem with multiple time windows. Transp Sci 47(1):53–63

  • 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):964–985

    Article  Google Scholar 

  • Sylejmani K (2013) Optimizing trip itinerary for tourist groups. Ph.D. thesis, Vienna University of Technology, Faculty of Informatics, Austria

  • 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 300–305

  • Tsiligirides T (1984) Heuristic methods applied to orienteering. J Oper Res Soc 35:797–809

  • Tumas G, Ricci F (2009) Personalized mobile city transport advisory system. Inf Commun Technol Tour 2009:173–183

    Google Scholar 

  • 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, Belgium

  • Vansteenwegen P, Souffriau W, Berghe GV, Oudheusden DV (2011a) The city trip planner: an expert system for tourists. Expert Syst Appl 38(6):6540–6546

    Article  Google Scholar 

  • Vansteenwegen P, Souffriau W, Oudheusden DV (2011b) The orienteering problem: a survey. Eur J Oper Res 209(1):1–10

    Article  Google Scholar 

  • 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):3281–3290

    Article  Google Scholar 

  • 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 16

Download references

Acknowledgements

This work is partially supported by a national research grant from the Ministry of Education, Science and Technology of the Republic of Kosova, as part of the research project entitled Tourist Tour Planning and Social Network Analysis. In addition, the authors would like to thank three anonymous reviewers, whose valuable comments helped in improving the content and presentation of this paper.

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Prishtina, Rr. “George Bush”, p.n., Prishtina, 10000, Republic of Kosovo

    Kadri Sylejmani

  2. E-Commerce Group, Institute of Software Technology and Interactive Systems (188), Faculty of Informatics, Vienna University of Technology, Favoritenstrae 9-11, 1040, Vienna, Austria

    Jürgen Dorn

  3. Database and Artificial Intelligence Group, Faculty of Informatics, Vienna University of Technology, Favoritenstrae 9-11, 1040, Vienna, Austria

    Nysret Musliu

Authors
  1. Kadri Sylejmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jürgen Dorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nysret Musliu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kadri Sylejmani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sylejmani, K., Dorn, J. & Musliu, N. Planning the trip itinerary for tourist groups. Inf Technol Tourism 17, 275–314 (2017). https://doi.org/10.1007/s40558-017-0080-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40558-017-0080-9

Keywords

Search

Navigation