skip to main content
10.1145/3185089.3185142acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicscaConference Proceedingsconference-collections
research-article

A Development of Travel Itinerary Planning Application using Traveling Salesman Problem and K-Means Clustering Approach

Authors Info & Claims
Published:08 February 2018Publication History

ABSTRACT

In this paper, an algorithm for making travel itinerary using traveling salesman problem (TSP) and k-means clustering technique is proposed. We employ the algorithm to develop a web based application that can help travelers to plan their travel itinerary. The developed application should be able to provide an optimal itinerary recommendation in terms of distance and travel time. We use initial assumption that the traveler has determined all the tourist destinations he/she wants to visit and also the number of days he/she will stay in the region. Our approach consists of two steps, macro grouping using k-means and micro tour arrangement using TSP. Yogyakarta city, one of the tourist city in Indonesia, is used as an example to illustrate how the proposed algorithm can help travelers make their itinerary. This approach works well in small to medium number points of interest. However, the application still need many improvements such as to make it run faster and to handle the additional constraints that exist when creating an itinerary.

References

  1. Hsu, F. C. and Chen, P. 2000. Interactive genetic algorithms for a travel itinerary planning problem. TSP, 1, 13.Google ScholarGoogle Scholar
  2. Russell, S. and Norvig, P. 1995. Artificial Intelligence A Modern Approach. Prentice-Hall, Egnlewood Cliffs, 25, 27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Hoffman, K. L., Padberg, M., and Rinaldi, G. 2013. Traveling salesman problem. In Encyclopedia of operations research and management science (pp. 1573-1578). Springer US.Google ScholarGoogle Scholar
  4. Cook, W. 2007. History of the TSP. http://www.math.uwaterloo.ca/tsp/history/index.html.Google ScholarGoogle Scholar
  5. Moon, C., Kim, J., Choi, G., and Seo, Y. 2002. An efficient genetic algorithm for the traveling salesman problem with precedence constraints. European Journal of Operational Research, 140(3), 606-617.Google ScholarGoogle ScholarCross RefCross Ref
  6. Razali, N. M. and Geraghty, J. 2011. Genetic algorithm performance with different selection strategies in solving TSP. In Proceedings of the world congress on engineering (Vol. 2, pp. 1134-1139).Google ScholarGoogle Scholar
  7. Dorigo, M. and Gambardella, L. M. 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53-66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chen, S. M. and Chien, C. Y. 2011. Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Expert Systems with Applications, 38(12), 14439-14450. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Murty, M. N. and Devi, V. S. 2011. Pattern recognition: An algorithmic approach. Springer Science & Business Media.Google ScholarGoogle Scholar
  10. Abbaspour, R. A. and Samadzadegan, F. 2011. Time-dependent personal tour planning and scheduling in metropolises. In Expert Systems with Applications, 38 (2011) 12439--12452. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Rizzo, C. 2017. This App Will Help You Effortlessly Plan a Multi-city European Vacation. http://www.travelandleisure.com/travel-tips/mobile-apps/eightydays-app-plans-european-vacation.Google ScholarGoogle Scholar
  12. Hagen, K., Kramer, R., Hermkes, M., Schumann, B., and Mueller, P. 2005. Semantic matching and heuristic search for a dynamic tour guide. Information and Communication Technologies in Tourism 2005, pp.149-159.Google ScholarGoogle Scholar
  13. Statistik Kepariwisataan 2016. https://visitingjogja.com/10193/statistik-pariwisata-2016/.Google ScholarGoogle Scholar
  14. Kauffman, L. and Rousseeuw, P. J. 1990. Finding Group in Data: An Introduction to Cluster Analysis. Wiley, New York.Google ScholarGoogle Scholar

Index Terms

  1. A Development of Travel Itinerary Planning Application using Traveling Salesman Problem and K-Means Clustering Approach

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICSCA '18: Proceedings of the 2018 7th International Conference on Software and Computer Applications
      February 2018
      349 pages
      ISBN:9781450354141
      DOI:10.1145/3185089

      Copyright © 2018 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 February 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader