# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 1 Introduction  
The tourist industry has been rapidly growing over the last years and is intensely integrated with modern information and communication technologies. According to the UNWTO (United Nations World Tourism Organization) ([[https://www.e-unwto.org/doi/pdf/10.18111/9789284421152](https://www.e-unwto.org/doi/pdf/10.18111/9789284421152)]([https://www.e-unwto.org/doi/pdf/10.18111/9789284421152](https://www.e-unwto.org/doi/pdf/10.18111/9789284421152))) around 1.5 billion international tourist arrivals were recorded in 2019 worldwide. The number of international travels is expected to increase by 3.3% per year between 2010 and 2030. The IT industry has begun to interact with all areas of the economy including tourism over time. The fusion of information technologies and tourism has created the phenomenon of smart tourism [1]. It is typical for smart tourism to collect and analyze data extracted from different sources in combination with the use of advanced information technologies to transform travel experiences [2] to make it more enriched, efficient and sustainable [3]. The different smart tourism services (any service that can be used by the tourist to automate tourism-based tasks based on artificial intelligence technologies), such as recommendation services, attraction itinerary construction services, improve the travel experience [4]. Tourist behaviour analysis is a popular direction of scientific research. According to Reference [5] the three large groups of people can benefit from the results of analysis: tourists themselves, public sector managers and businessmen. Tourists can use behaviour analysis for personal satisfaction and growth indirectly by using enhanced tourism services. Public sector managers can manage impacts and generate community benefits and businessmen can improve marketing, sales and management by using tourist behaviour analysis. More and more travelers use smartphones on their trips and that has a huge impact on the way tourists construct their attraction visiting plans [6]. The amount of user generated content (digital content which produced and shared by tourists in the online world) is increasing from year to year. 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. These models can be used for improving smart tourism services. This paper presents a tourist behaviour analysis system, which defines the whole process of tourist behaviour analysis: data sources and tourist behaviour component identification; a way of storing and presenting heterogeneous data and behaviour analysis instruments. Within the system, the tourist \(T\) can be described as a person who visits a certain set of POIs (point of interest-\(p_{i},\ldots,p_{N}\), where \(p_{i}\) represents a POI) in the region. Tourist behaviour can be described by the following parameters:  
\[B=\{\textit{It};\textit{Pr};\textit{Rt};\textit{Ct}\}, \tag{1}\]  
where \(\textit{It}\)--POI visiting plan; \(\textit{Pr}\)--POI preferences; \(\textit{Rt}\)--tourist route characteristics; tracked by sensors \(s_{1},...,s_{m}\), where \(s_{j}\) --tourist device sensor; \(\textit{Ct}\)--tourist generated content. The authors propose the digital pattern of life concept which represents the connection between tourist and different aspects of the tourist in smart services. The usage of the digital pattern of life simplifies the tourist behaviour model construction by providing a unified interface for data retrieval. As an example, the authors describe the three case studies of behaviour analysis based on the data taken from the digital pattern of life. Artificial neural networks [11] (ANN) were used for model construction.