Predicting Travel Behaviour of International and Domestic Tourists using Big Data
N. Padmaja1, T. Sudha2
1N. Padmaja, Research Scholar, Department of Computer Science and Engineering, School of Engineering & Technology, Sri Padmavati Mahila Visvavidyalayam, 517502, Andhra Pradesh, India.
2T. Sudha, Professor, Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, 517502, Andhra Pradesh, India
Manuscript received on 5 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 1572-1580 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4324098319/2019©BEIESP | DOI: 10.35940/ijrte.C4324.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Tourism is one of the most important sectors contributing towards the economic growth of India. Big data analytics in the recent times is being applied in the tourism sector for the activities like tourism demand forecasting, prediction of interests of tourists’, identification of tourist attraction elements and behavioural patterns. The major objective of this study is to demonstrate how big data analytics could be applied in predicting the travel behaviour of International and Domestic tourists. The significance of machine learning algorithms and techniques in processing the big data is also important. Thus, the combination of machine learning and big data is the state-of-art method which has been acclaimed internationally. While big data analytics and its application with respect to the tourism industry has attracted few researchers interest in the present times, there have been not much researches on this area of study particularly with respect to the scenario of India. This study intends to describe how big data analytics could be used in forecasting Indian tourists travel behaviour. To add much value to the research this study intends to categorize on what grounds the tourists chose domestic tourism and on what grounds they chose international tourism. The online datasets on places reviews from cities namely Chicago, Beijing, New York, Dubai, San Francisco, London, New Delhi and Shanghai have been gathered and an associative rule mining based algorithm has been applied on the data set in order to attain the objectives of the study.
Keywords: Associative Rule mining Algorithm , Big Data Analytics, Indian tourists, International tourist, Tourist Behaviour,
Scope of the Article: Behaviour of Structures.