A Season-Wise Long-term Travel Spots Prediction Based on Markov Chain Model in Smart Tourism - Ahmad, Shabir; Kim, Do-Hyeun (International Journal of Engineering and Technology, 2018)
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.Â Â Â