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Tourism Market of the Russian Federation: Analysis of Interactions between Outbound and Domestic Tourism using Neural Networks
Background/Objectives: The article studies the interaction between outbound and domestic tourism in Russia in the context of an unstable economic situation. Methods/Statistical Analysis: The national and regional tourism development indicators are mostly analyzed and forecast by traditional statistical methods (multiple linear regression analysis, time series analysis methods, adaptive prediction). These methods give good results in the absence of changes in the current development trends and provided the structure of the socio-economic system to be analyzed is preserved. Findings: A new research technique using neural networks and neural network models has been developed, enabling to consider changes in the structure of tourist flows and nonlinear relationships between the studied parameters. The official statistics on the tourism and hospitality, website data aggregating information about search queries of tourists were used. The study results allow qualitatively assessing the impact of the closure of the popular tourist destinations on the outbound and domestic tourism. Improvements: Based on the research results it can be argued that a reduction in outbound tourist flow does not lead to the automatic increase in domestic tourism. The main factor of tourism services market development is the amount of effective demand.
Neural Networks, Neural Network Models, Tourism, Tourist Flow, Tourism Business.
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