A Season-Wise Long-term Travel Spots Prediction Based on Markov Chain Model in Smart Tourism

  • Authors

    • Shabir Ahmad
    • Do-Hyeun Kim
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.24377
  • Tourism Management, Markov Chain, Stochastic Prediction, Locations Forecasting, Steady-state Vector, Transition Matrix

  • Background/Objectives: Tourism plays a pivotal role in the development of a country and prediction of tourist movement can help in devising sustainable policies which in turn benefit in promoting and developing tourism. The movements of sightseers can help in estimating the amount of revenue generated for that particular sight. However, the preferences of these sightseers may change overtime and depends greatly on seasons. Therefore, a sight considered famous for one season is not that much popular for another season.

    Methods/Statistical analysis: This paper proposes a probabilistic approach using Markov Chain model to predict the recommended places of Jeju Island for each season. Real data is collected from different locations using routers which are installed on each location. The proposed method is evaluated on the data and the tourist places are recommended for each season based on the steady state probabilities of each spot.

    Findings: By applying the Markov Chain Model, the proposed approach accurately analyses the stochastic behavior of the data and recommend the places for each season which are predicted to be the top visiting places. The top places which are found to be equally popular across all seasons are Seongsan Ilchul and Seopjikoji.

    Improvements/Applications: The data is analyzed with respect to the collected data and the popularities of the locations are found without considering the reason of the population. An improvement could be considering distance and other factors to correlate with the popularity of a certain location.

     

     

     

  • References

    1. [1] Hu YC. Predicting foreign tourists for the tourism industry using soft computing-based Grey–Markov models. Sustainability. 2017 Jul 13;9(7):1228.

      [2] Mao S, Tu E, Zhang G, Rachmawati L, Rajabally E, Huang GB. An automatic identification system (AIS) database for maritime trajectory prediction and data mining. InProceedings of ELM-2016 2018 (pp. 241-257). Springer, Cham.

      [3] Gonzalez H, Han J, Li X, Myslinska M, Sondag JP. Adaptive fastest path computation on a road network: a traffic mining approach. InProceedings of the 33rd international conference on Very large data bases 2007 Sep 23 (pp. 794-805). VLDB Endowment.

      [4] Haldankar I, Tiwari M, Usha G, Aruna S. An Intelligent Framework for Road Safety and Driver Behavioral Change Detection System Using Machine Intelligence. InComputational Vision and Bio Inspired Computing 2018 (pp. 913-924). Springer, Cham.

      [5] Li L, Miao J, Fang S, Yang IH, Wang J, inventors; Baidu USA LLC, assignee. Method and system to predict vehicle traffic behavior for autonomous vehicles to make driving decisions. United States patent application US 15/359,466. 2018 May 24.

      [6] Giannotti F, Nanni M, Pinelli F, Pedreschi D. Trajectory pattern mining. InProceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining 2007 Aug 12 (pp. 330-339). ACM.

      [7] Collins L. An introduction to Markov chain analysis. Geo Abstracts Ltd..

      [8] Morcous G. Performance prediction of bridge deck systems using Markov chains. Journal of performance of Constructed Facilities. 2006 May;20(2):146-55.

      [9] Lounis Z. Reliability-based life prediction of aging concrete bridge decks. Life prediction and aging management of concrete structures. 2000:229-38.

      [10] Morcous G, Rivard H. Computer assistance in managing the maintenance of low-slope roofs. Journal of computing in civil engineering. 2003 Oct;17(4):230-42.

      [11] Golabi K, Shepard R. Pontis: A system for maintenance optimization and improvement of US bridge networks. Interfaces. 1997 Feb;27(1):71-88.

      [12] Thompson PD, Small EP, Johnson M, Marshall AR. The Pontis bridge management system. Structural engineering international. 1998 Nov 1;8(4):303-8.

      [13] Cesare MA, Santamarina C, Turkstra C, Vanmarcke EH. Modeling bridge deterioration with Markov chains. Journal of Transportation Engineering. 1992 Nov;118(6):820-33

  • Downloads

  • How to Cite

    Ahmad, S., & Kim, D.-H. (2018). A Season-Wise Long-term Travel Spots Prediction Based on Markov Chain Model in Smart Tourism. International Journal of Engineering & Technology, 7(4.39), 564-570. https://doi.org/10.14419/ijet.v7i4.39.24377