Abstract
The tourism industry has been rapidly growing over the last years and IT technologies have had a great affect on tourists as well. Tourist behaviour analysis has been the subject of different research studies in recent years. This paper presents the digital pattern of life concept which simplifies the tourist behaviour models’ construction and usage. The digital pattern of life defines the general concepts of tourist behaviour, connects the tourist and the digital world and allows us to track behaviour changes over time. A literature review of the current state of the research in selected fields is performed for identifying the existing problems. The case studies of behaviour analysis based on classification, clustering and time series events behaviour models are shown. An ontological approach and artificial neural networks are used during behaviour model construction, training and evaluation. The gathered results can be used by smart tourism service developers and business stakeholders.
Highlights
The tourist industry has been rapidly growing over the last years and is intensely integrated with modern information and communication technologies
The rest of the paper is structured as follows: the authors in Section 2 describe the current state of the research field and show similarities and issues of the proposed tourist analysis systems and approaches; Section 3 presents the concept of a developed tourist behaviour analysis system with the digital pattern of life usage; Section 4 presents information about tourist behaviour analysis case studies such as classification, clustering and time series prediction with artificial neural networks usage; Section 5 discusses the obtained results and in Section 6, the work is concluded and future work is explained
This paper presents the related work analysis in scope of tourist behaviour analysis, presents the tourist behaviour analysis system based on the usage of the digital pattern on life concept and shows the three case studies of system usage based on Artificial neural networks [11] (ANN) models
Summary
The tourist industry has been rapidly growing over the last years and is intensely integrated with modern information and communication technologies. Scientists use Big Data methods and techniques [7] to handle a large amount of open information [8,9,10] Different sources, such as user generated content (photo/videos/attractions reviews), social networks and different sensors from smart gadgets, can be used as the basis for tourist behaviour prediction models. The rest of the paper is structured as follows: the authors in Section 2 describe the current state of the research field and show similarities and issues of the proposed tourist analysis systems and approaches; Section 3 presents the concept of a developed tourist behaviour analysis system with the digital pattern of life usage; Section 4 presents information about tourist behaviour analysis case studies such as classification, clustering and time series prediction with artificial neural networks usage; Section 5 discusses the obtained results and, the work is concluded and future work is explained The rest of the paper is structured as follows: the authors in Section 2 describe the current state of the research field and show similarities and issues of the proposed tourist analysis systems and approaches; Section 3 presents the concept of a developed tourist behaviour analysis system with the digital pattern of life usage; Section 4 presents information about tourist behaviour analysis case studies such as classification, clustering and time series prediction with artificial neural networks usage; Section 5 discusses the obtained results and in Section 6, the work is concluded and future work is explained
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