适应性旅游建模:区域层面的经验、问题和应用前景

IF 0.4 Q3 AREA STUDIES
A. Aleksandrova, V. Dombrovskaya
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引用次数: 1

摘要

介绍新冠肺炎疫情造成的旅游业危机强调了俄罗斯旅游业发展中存在的区域不对称性。尽管各地区的旅游和娱乐潜力各不相同,但工业和酒店业效率如此显著差异的主要原因在于旅游活动管理。区域政策最重要的工具是预测。本文的目的是考虑基于官方统计数据的区域大规模旅游研究中自适应模型的预测能力。材料和方法。该研究基于自适应建模方法,该方法已证明可以获得在不确定性下发展的大量小样本的短期预测。作为建模对象的是贝加尔湖地区旅游活动的一系列动态指标。建模基于区域旅游业固有的序列,该序列具有明显的季节性成分,时间序列具有年度指标,在分解过程中只检测到趋势成分。后果自适应模型显示出了很高的预测能力,但在最后一个时间步长期间,由于引入了对游客流动的限制,导致指标急剧崩溃的系列除外。在这些条件下的模型客观上没有时间适应。如果有一种暂时的“学习”的可能性,那么即使对正在研究的测试急剧下降的预测也有很高的准确性。讨论和结论。根据研究结果,利用自适应建模预测区域层面旅游活动一系列动态指标的可能性得到了证实,这些指标在经历急剧变化的不确定性条件下。这项工作的结果可能对区域政策领域的专家,特别是旅游管理部门、企业界的雇员以及相关领域的科学和教学人员有用,并可用于培养旅游业高等和中等职业教育专家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Tourism Modeling: Experience, Problems and Prospects of Application at the Regional Level
Introduction. The crisis of the tourist industry caused by the COVID-19 pandemic emphasized the existing regional asymmetry in the development of Russian tourism. Despite the diversity of tourist and recreational potentials in the regions, the main reason for such significant differences in the efficiency of the field of industry and hospitality lies in the tourist activity management. The most important tool for regional policies is forecasting. The purpose of the article is to consider the prognostic capabilities of adaptive models in relation to tourist studies at a regional large-scale level based on data from official statistics. Materials and Methods. The study is based on the adaptive modeling method, which has proven itself to obtain short-term forecasts of a number of small samples developing under uncertainty. As the objects of modeling were the series of the dynamics of indicators characterizing tourist activities in the Baikal region. Modeling was based on the series inherent in the regional tourism with a pronounced seasonal component and time series with annual indicators, where only the trend component is detected during decomposition. Results. Adaptive models have shown high prognostic capabilities with the exception of series in which a sharp collapse of the indicator caused in this case by the introduction of restrictions on tourist mobility occurs during one last time step. The model under these conditions objectively does not have time to adapt. If there is a temporary possibility of to “learning”, the forecast even of a sharp decline in the tests under study has a confirmed high accuracy. Discussion and Conclusion. According to the results of the study, it is confirmed by the possibility of using adaptive modeling to predict the series of dynamics of tourist activity indicators at the regional level, undergoing sharp changes in the conditions of uncertainty. The results of the work may be useful to specialists in the field of regional policies, in particular to employees of tourist administrations, a business community, as well as scientific and pedagogical personnel in the relevant area and can be used in the preparation of specialists of higher and secondary vocational education in tourism.
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