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Improvement of “Multiple Sightseeing Spot Scheduling System”

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Computational Science/Intelligence and Applied Informatics (CSII 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 726))

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Abstract

Recently, the number of foreign tourists and travelers coming to Japan is increasing explosively. In 2006, 10 years later, the number of the foreign visitors was approximately 7.3 million people, and it reached approximately 24 million people in 2016. The reason is various Japanese tourism policies such as “Visit Japan Project” and “Cool Japan”. As a result, Japan’s travel and tourism competitiveness of 2015 was one of the highest in the world. However, a problem is still left in the current Japanese sightseeing situation. For example, there are not many stores with foreign language correspondence. Also there are not many places where they can pay with a credit card. Furthermore, the traffic such as the subway or the buses is complicated. In addition, recently diversification advances for a current Japanese sightseeing method. In the conventional sightseeing, a sightseeing method to visit the famous sightseeing spots listed on the Internet and a guidebook was common. However, particularly these days, as well as a famous sightseeing spots, the number of foreign tourists and travelers to visit the “local-based type” stores such as Japanese bar or Japanese diner, has been increasing intensively. It is not easy to find those kind of places. To solve the problem, we develop “Multiple Sightseeing Spot Scheduling System” that enables a guide of new sightseeing in this research. In this paper, we consider an improvement plan for the application under development and implement the plan.

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References

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 17K00730

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Correspondence to Kazuya Murata .

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Murata, K., Fujimoto, T. (2018). Improvement of “Multiple Sightseeing Spot Scheduling System”. In: Lee, R. (eds) Computational Science/Intelligence and Applied Informatics. CSII 2017. Studies in Computational Intelligence, vol 726. Springer, Cham. https://doi.org/10.1007/978-3-319-63618-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-63618-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63617-7

  • Online ISBN: 978-3-319-63618-4

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