Tourism Market of the Russian Federation: Analysis of Interactions between Outbound and Domestic Tourism using Neural Networks

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1 Indian Journal of Science and Technology, Vol 9(27), DOI: /ijst/2016/v9i27/97698, July 2016 ISSN (Print) : ISSN (Online) : Tourism Market of the Russian Federation: Analysis of Interactions between Outbound and Domestic Tourism using Neural Networks Konstantin Aleksandrovich Miloradov* and Galina Mikhailovna Eidlina Plekhanov Russian University of Economics, Moscow, Russian Federation; M_k_a.rea@yandex.ru; gm20051@yandex.ru Abstract 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. Keywords: Neural Networks, Neural Network Models, Tourism, Tourist Flow, Tourism Business 1. Introduction Tourism is one of the most important parts of the modern service sector and plays a significant role in the global economy. According to the World Tourism Organization (UNWTO), in 2014 there was an increase in the overall tourism: the number of international tourist arrivals compared to 2013 year increased totaling to billion people, the volume of tourism services market reached USD bln worldwide 1. In the Russian Federation the contribution of tourism and hospitality in the economy remains significantly below the world average values for a sufficiently long period of time. Ensuring the quality and accessibility of tourism services is one of the objectives of social and economic development according to the Concept of Long-Term Socio-Economic Development of the Russian Federation for the Period till In the same document, laying the foundations of modern industry of tourism and recreational services and increase in its competitiveness in the international market is noted as one of the main priorities of the social and economic policy. Tourism business in Russia has great potential for development. Such forms of tourism as eco-tourism, sports tourism, event tourism, ethnic tourism, as well as various kinds of extreme tourism are promising. However, some weaknesses such as underdeveloped tourism and recreation infrastructure, transport and logistics *Author for correspondence

2 Tourism Market of the Russian Federation: Analysis of Interactions between Outbound and Domestic Tourism using Neural Networks infrastructure, level of services being low by the world standards hinder the implementation of potentially successful and profitable destinations reduce the attractiveness of domestic tourism for foreign and Russian tourists. The implementation of mega-projects, which include the Winter Olympic Games in Sochi in 2014, the 2018 FIFA World Cup and a number of others, is one of the mechanisms for the tourism and hospitality development in the Russian Federation. Among the causes that have recently (within 2-3 years) adversely affected the Russian tourist market and Russian tourists, the following should be noted: Worsening of the economic situation and the decrease in the income level of a significant part of the Russian Federation citizens. Change of the internal rules that determine the ability to travel abroad for certain categories of the Russian citizens. Changes in the international situation and the increase in risks for the tourist business. In particular, recently such popular tourist destinations such as Egypt and Turkey happened to be virtually inaccessible. Therefore, it is relevant to develop and improve the procedures for tourist services market research in an unstable economic situation and to analyze the factors influencing the outbound tourist flows and the interaction of outbound and domestic tourism, the results of solving this vital task can be used to predict performance and plan development of the national tourism sector. 2. Methods The analysis and forecasting of tourism development indicators at the national and regional levels is mostly carried out 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 in the conditions of preserving the structure of the socio-economic system whose parameters are analyzed. A number of publications, for example 3-13, describe the possibilities of modern technologies of mining into databases and examples of their application at the tourism and hospitality industry facilities, in particular, data mining Figure 1. Closure of tourist destinations. Legend: 1 - Tourist flow 2 - Domestic tourist flow 3 - Outbound tourist flow - closed destinations and business intelligence methods to predict the flow of tourists in certain regions (Taiwan, Catalonia in Spain). The Russian travel services market is currently developing in conditions of high uncertainty associated with the unstable economic and financial situation and complicated international situation. Closure of such popular tourist destinations as Egypt and Turkey shown in Figure 1. has led to the change in the structure and volume of tourist flows. For the analysis of the factors influencing the outbound tourist flows, it is necessary to use techniques that are applicable to the unstable economic situation and the sharp fluctuations of the demand. The authors propose to use a technique with application of the data mining technology, namely, computer models based on Artificial Neural Networks (ANN) to analyze the factors affecting the volume of outbound tourist flow and to assess the interaction of outbound and domestic tourism in the conditions of an unstable economic situation, sharp fluctuations in exchange rates and demand for tourist services. 2 Indian Journal of Science and Technology

3 Konstantin Aleksandrovich Miloradov and Galina Mikhailovna Eidlina Figure 2. Neural network. The neural network structure is shown in Figure 2. The advantage of neural networks is the possibility of learning, which implies finding the neuron coupling coefficients. The trained neural network will give the correct result when using incomplete or partially distorted original data. Neural networks allow considering the non-linear couplings between the model parameters. Types of artificial neural networks (multilayer perceptron, radial basis function network, probabilistic neural network, generalized regression neural network, Kohonen network, etc.), their advantages and disadvantages are described in particular in the following publications Results The authors used the official statistics published on the websites of Russian Federal State Statistics Service and the Federal Tourism Agency. The data on the dynamics of the hospitality industry development in the Russian Federation as a whole are given in Table 1. These data show that such an important indicator as the dynamics of fixed asset investments aimed at the development of the collective accommodation facilities has increased significantly compared to However, the analysis by regions shows that at the same time the major part (RUB 53,664.9 mln) of the total investment RUB 75,991.6 mln in 2014 is accounted for the Krasnodar region. This is obviously connected with the preparations for the Winter Olympic Games in Sochi in Data to be analyzed using neural networks (yearly aggregated statistics about the state of the tourist market in the Russian Federation) are given in Table 2. The variables selected for analysis include average annual rates of major world currencies (USD and Euro), salary in the national currency (RUB), the consumer price index, the volume of outbound tourist flow (net of tourist flows to the closed destinations Egypt and Turkey), variables corresponding to volumes of domestic tourist flows to Egypt and Turkey, the volume of the hospitality industry in the Russian Federation are analyzed separately. The authors have developed computer models based on artificial neural networks. The topology of the artifi- Indian Journal of Science and Technology 3

4 Tourism Market of the Russian Federation: Analysis of Interactions between Outbound and Domestic Tourism using Neural Networks Table 1. Dynamics of the hospitality industry development indicators in the Russian Federation Indicator Area of the room stock of collective accommodation facilities, thous. sq.m Average headcount of staff of collective accommodation facilities, thous. persons 12, , , , , , Number of the RF citizens staying in the collective accommodation facilities, thous. persons 24, , , , , ,798.5 Number of foreign citizens staying in the collective accommodation facilities, thous. persons 30,55.4 3, , , , ,607.9 Amount of marketed services of hotels and similar accommodation facilities, RUB bln Fixed asset investments aimed at the development of the collective accommodation facilities (hotels, and other accommodation), RUB mln. 17, , , , , ,991.6 cial neural network is a Multi-Layer Perceptron (MLP). Models of neural networks, containing one, two and three hidden layers, have been constructed. Parameters of the developed neural network models are given in Table 3. The output variables are the Outbound Tourist Flow (TF) and Volume of hospitality services in the Russian Federation (Vol). The authors consider the neural network models and results obtained using these models as a basis for a qualitative analysis of the various scenarios for changing the structure of tourist flows. The results allow determining that the closure of such popular destinations as Turkey and Egypt (Turkey even to a greater extent), will lead to a significant reduction in outbound tourist flow, and other destinations will not fully compensate for their closure. In turn, the reduction in outbound tourist flow will not lead to an automatic increase in domestic tourism. The level of 4 Indian Journal of Science and Technology

5 Konstantin Aleksandrovich Miloradov and Galina Mikhailovna Eidlina Table 2. Statistical data on the state of the tourist market in the Russian Federation Year Average annual USD exchange rate Average annual Euro exchange rate Salary and wages, RUB Consumer price index, % Outbound tourist flow*, thous. travels Outbound tourist flow to Egypt, thous. travels Outbound tourist flow to Turkey, thous. travels Volume of hospitality services in the RF, RUB bln. USD EUR SalRub CPI TF TFE TFT Vol , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,054.6** 1,031.5** * Outbound tourist flow with no account for travels to Egypt and Turkey ** - data for the first half year of 2015 population incomes is the main factor affecting the state of the domestic market of tourist services. Another source of data to estimate the demand for a variety of tourist destinations are tourist searches on specialized websites on the Internet. Table 4 provides the data of Situational Center for Tourism Industry of the Russian State University of Tourism and Service (RSUTS) and Sletat.ru Company relating to search queries of Russian tourists. Data are presented on the website Turbarometr 27 a joint project of Turpomosch Association, Sletat.ru Company and the Russian State University of Tourism and Service. The project is based on the search engine information base of Sletat.ru Company and tracks changes in demand for tourist destinations. Indian Journal of Science and Technology 5

6 Tourism Market of the Russian Federation: Analysis of Interactions between Outbound and Domestic Tourism using Neural Networks Table 3. Parameters of neural networks Parameter 1 hidden layer 2 hidden layers 3 hidden layers Number of neurons in the hidden layers 4; 5 4; 5 1; 2 moment Hidden layer activation functions sigmoid sigmoid sigmoid Rate of learning Number of learning epochs 100-1, ,000 1,000-10,000 Table 4. Search queries of Russian tourists Place February 2015 Summer 2015 February 2016 Summer Egypt Turkey Thailand Greece 2 Thailand Egypt Viet Nam Spain 3 Viet Nam Greece India Russia 4 India Cyprus UAE Bulgaria 5 UAE Spain Dominican Republic Cyprus 6 Dominican Republic Russia Russia Thailand 7 Turkey Bulgaria Sri Lanka Montenegro 8 Czech Republic Thailand Cuba Tunisia 9 Cuba Tunisia Spain Italy 10 Italy Montenegro Israel Morocco According to data given in Table 4, the number of requests to search for tours abroad exceeds the number of queries for recreation and leisure in Russia. This indicates that the outbound tourism services are still in demand by Russian citizens, and reduction of outbound tourist flow, what is observed in recent times, does not lead to an automatic increase in domestic tourism. 4. Discussion In an unstable economic environment for the study of trends in the development of such industries as tourism it is not sufficient to use only traditional statistical forecasting methods, the majority of which is related to the construction of models based on certain assumptions 6 Indian Journal of Science and Technology

7 Konstantin Aleksandrovich Miloradov and Galina Mikhailovna Eidlina and theoretical conclusions (for example, that the desired relationship is linear or some variable has a normal distribution), enabling to extrapolate the existing trends. As noted in 22, the approach based on the use of neural networks is not associated with such assumptions, it is equally suitable for linear and complex nonlinear dependencies, and is particularly effective in the exploratory analysis of the data, when it is necessary to find out whether there are dependencies between variables. The authors believe that it is required to develop new techniques and computer models applicable in a volatile economic environment, which would take into account the instability of relationships of the socio-economic system under study and the non-linear nature of these relationships. These models are computer simulations of organizational and economic systems, including options of data mining, in particular, with the help of artificial neural networks. These computer models have the following advantages: They allow taking into account not only financial and economic factors, but also other (political, environmental) factors; They allow using not only the official statistics, but also other kinds of information data acquired from specialized websites on the top search queries as data sources for the analysis of tourism development indicators in the context of foreign tourist destinations, and the Russian regions; They allow analyzing a complex socio-economic system, which is the market of tourist services, in conditions of reorganization of its structure (for example, when new tourist destinations appear or the popular ones are closed). The proposed models can be improved by including factors that take into account the average price of tours to the most popular destinations. Also a topical area of research in the near future is to study the impact of event tourism (the World Cup in 2018) on the development of the tourism market in the Russian Federation. 5. Conclusions The results of researching the tourist services market in the Russian Federation with the help of the developed neural network models suggest the following conclusions: The role of domestic tourism and hospitality services market increases for the national tourism industry in the context of the ongoing economic downturn and a complicated international environment. Reduction of outbound tourist flow happening in recent times does not lead to an automatic increase in domestic tourism. The level of incomes is the main factor affecting the state and prospects for further development of the domestic tourist services market. Closure of such popular destinations as Turkey and Egypt has resulted in a significant decrease in outbound tourist flow. Other foreign destinations will not fully compensate for their closure. Closure of the most popular destinations, such as Turkey and Egypt, will lead to reduced competition and increased prices for travel services in the domestic market of the Russian Federation for a sufficiently long period of time. 6. References 1. UNWTO: 2014 s Tourism Highlights Available from: s-tourism-highlights/ 2. The Concept of Long-Term Socio-Economic Development of the Russian Federation for the Period till Available from: 3. Fernandes P, Teixeira J. New approach of the ANN methodology for forecasting time series: use of time index. Proceeding of ICTDM; Kos-Greece p Available from: 4. Claveria O, Torra S. Forecasting tourism demands to Catalonia: Neural networks vs. time series models. Economic Modeling. 2014; 36: Coshall JT, Charlesworth R. A management orientated approach to combination forecasting of tourism demand. Tourism Management. 2010; 32: Lin CJ, Chen HF, Lee TS. Forecasting tourism demand using time series, artificial neural networks and multivariate adaptive regression splines: Evidence from Taiwan. International Journal of Business Administration. 2011; 2: Belianskiy VP, Miloradov KA. Application of business analysis technologies at the hospitality industry facilities. RISK: Resources, Information, Supply, Competition. 2013; 4: Indian Journal of Science and Technology 7

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