Intelligent tour planning system using crowd sourced data
Автор: Md. Saef Ullah Miah, Md. Masuduzzaman, Williyam Sarkar, H M Mohidul Islam, Faisal Porag, Sajjad Hossain
Журнал: International Journal of Education and Management Engineering @ijeme
Статья в выпуске: 1 vol.8, 2018 года.
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To observe the beauty of nature and to visit various places around the world, a vast number of tourists visit different countries and many tourist attraction sites now-a-days. But Most of the tourist places have failed to introduce itself as a tourist destination to the visitor due to lack of proper information and proper guideline to visit there. This paper tries to focus on some problems in the tourism industry and try to solve those problems using crowd sourced data with some customized algorithms. Some of the main problems are the lack of information about a destination tourist spot, combination on budget to visit the spot, time of travels etc. We proposed a customize algorithm which will provide maximum suggestion to visit a place with nearest all sub place based on user destination within their given budget and time. Using our method, user can choose the most suitable plan for them to visit those places.
Data Crowdsourcing, Tourist, BFS, DFS, Dijkstra, Tour Planner, Route Suggestion
Короткий адрес: https://sciup.org/15015751
IDR: 15015751 | DOI: 10.5815/ijeme.2018.01.03
Список литературы Intelligent tour planning system using crowd sourced data
- Fatima Leal, Benedita Malheiro and Juan Carlos Burguillo, “Recommendation of Tourism Resources Supported by Crowdsourcing” in DOI: 10.13140/RG.2.2.30159.69283 Conference: International Conference on Information and Communication Technologies in Tourism 2016 (ENTER 2016), At Bilbao, Spain, and Volume: ENTER 2016 PhD Workshop, International Conference on Information and Communication Technologies in Tourism 2016, 18-25.
- Jiang, H., 2013. The Research Review of Intelligent Tourism. Journal of Management and Strategy, 4(4), p.65.
- Harrill, R., 2004. Residents’ attitudes toward tourism development: A literature review with implications for tourism planning. CPL bibliography, 18(3), pp.251-266.
- Becken, S. and Wilson, J., 2007. Trip Planning and Decision Making of Self-Drive Tourists— Quasi-Experimental Approach. Journal of Travel & Tourism Marketing, 20(3-4), pp.47-62.
- Cohen, S.A., Prayag, G. and Moital, M., 2014. Consumer behaviour in tourism: Concepts, influences and opportunities. Current Issues in Tourism, 17(10), pp.872-909.
- Gunn, C.A. with Var, T. (2002) Tourism Planning: basics, concepts and cases, 4th Edition, London: Routledge.
- Lai, K., Li, Y. and Feng, X., 2006. Gap between tourism planning and implementation: A case of China. Tourism Management, 27(6), pp.1171-1180.
- Buhalis, D., 1998. Strategic use of information technologies in the tourism industry. Tourism management, 19(5), pp.409-421
- Baggio, R., 2008. Symptoms of complexity in a tourism system. Tourism Analysis, 13(1), pp.1-20.
- Tarjan, R., 1972. Depth-first search and linear graph algorithms. SIAM journal on computing, 1(2), pp.146-160.
- Yuejin, Y., Zhoujun, L. and Huowang, C., 2005. A depth-first search algorithm for mining maximal frequent itemsets [J]. Journal of Computer Research and Development, 3, p.015.
- Sharma, M.B., Mandyam, N.K. and Iyangar, S.S., 1989, February. An optimal distributed depth-first-search algorithm. In Proceedings of the 17th conference on ACM Annual Computer Science Conference (pp. 287-294). ACM.
- Zhou, R. and Hansen, E.A., 2006. Breadth-first heuristic search. Artificial Intelligence, 170(4-5), pp.385-408.
- Klöckner, K., Wirschum, N. and Jameson, A., 2004, April. Depth-and breadth-first processing of search result lists. In CHI'04 extended abstracts on Human factors in computing systems (pp. 1539-1539). ACM.
- Broumi, S., Bakal, A., Talea, M., Smarandache, F. and Vladareanu, L., 2016, November. Applying Dijkstra algorithm for solving neutrosophic shortest path problem. In Advanced Mechatronic Systems (ICAMechS), 2016 International Conference on (pp. 412-416). IEEE
- Aggarwal, C. and Yu, S., 2005. An effective and efficient algorithm for high-dimensional outlier detection. The VLDB Journal—The International Journal on Very Large Data Bases, 14(2), pp.211-221
