冠状病毒疫情期间入境游客数量预测及客源市场分析

Xia Jiang, Bin Zhao
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引用次数: 0

摘要

随着我国经济的快速发展,入境旅游市场的竞争也越来越激烈。入境旅游可持续发展的关键是保证一定数量的游客。因此,预测入境游客数量,研究入境游客市场是重要的一步。作为中国的旅游龙头城市,如何吸引更多的游客,不仅关系到上海入境旅游的发展,也为其他城市在新冠肺炎疫情期间提供了一些启示。本文采用改进的灰色马尔可夫(GM)模型对新冠肺炎疫情期间上海入境游客数量进行预测,并采用偏差份额分析法对入境游客市场变化进行研究。最后,通过集合经验模态分解分析了上海入境游客的时间尺度特征和趋势。GM(1,1)模型是灰色系统理论中应用最广泛的灰色动态预测模型之一,它由一个单变量一阶微分方程组成。初始值修正改进了灰色GM(1,1)模型,并在状态划分中引入中心点三角形白化权函数来改进Markova模型。通过与传统GM(1,1)、初值修正GM(1,1)和传统灰色马尔可夫预测模型的预测结果对比,验证了该模型的预测效果较好。这些模型优于线性回归和时间序列。偏差份额分析探讨了入境旅游市场的变化,结果表明,2004 - 2017年,上海入境旅游市场的发展速度高于全国,结构更加合理和具有竞争力。除日本外,各国到全国和上海的入境游客数量都有所增加,而且增幅很大。采用集合经验模态分解方法,分析了上海入境游客的时间尺度特征和变化趋势。研究结果表明:第一,入境游客总数和出境游客数量主要在3个月或6个月内发生变化,而港澳台地区则在高频和低频之间波动。第二,主要是周期性波动和来源国无明显趋势。日本、泰国、英国、法国、德国的波动周期为3个月;澳门是3、6、12、60、180个月;新加坡是3,6,180个月。第三,有明显的趋势和周期波动作为对来源国的补充。香港的波动周期分别为3个月、6个月、90个月和180个月;在台湾、加拿大和俄罗斯是3、6个月;印度尼西亚、美国、意大利和新西兰分别为3个月、6个月和12个月;马来西亚是3180个月;在韩国是345个月;在澳大利亚是4到7个月。台湾、加拿大、俄罗斯和新西兰的上升趋势最为显著。根据上述研究结果,可以根据预测的上海入境游客数量、客源国市场结构以及客源国的周期性波动和趋势,对上海入境旅游产业提出具体的市场结构竞争建议和策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Forecast of The Number of Inbound Tourists and The Analysis of The Source Market During The Epidemic of Coronavirus Disease
With the rapid development of economy, the competition of inbound tourism market is more and more fierce. The key point of sustainable development of inbound tourism is to ensure a certain number of tourists. Therefore, it is an important step to predict the number of inbound tourists and study the market of inbound tourists. As a leading tourism city in China, how to attract more tourists is not only related to the development of inbound tourism in Shanghai, but also provides some inspiration for other cities during the epidemic of Coronavirus Disease. In this paper, an improved grey markov (GM) model is used to predict the number of inbound tourists in Shanghai during the epidemic of Coronavirus Disease, and then the market changes of inbound tourists are studied by the deviation-share analysis method. Finally, the tim-scale characteristics and trends of inbound tourists in Shanghai are analyzed by ensemble empirical mode decomposition. GM (1,1) model is one of the most widely used grey dynamic prediction models in grey system theory, which is composed of a first order differential equation with a single variable. The initial value correction improves the gray GM (1,1) model, and introduces the center point triangle albino weight function in the state division to improve the Markova model. Comparing with the results of traditional GM (1,1), initial value modified GM (1,1) and traditional grey markov prediction models, the prediction effect of this model is verified to be better. These models are better than linear regression and time series. Deviation-share analysis explores the changes in the inbound tourist market, and the results show that from 2004 to 2017, the inbound tourist market in Shanghai developed faster than that in the whole country, with a more reasonable and competitive structure. In addition to Japan, the number of inbound tourists from each country to the whole country and Shanghai has increased and increased greatly. The time-scale characteristics and trends of inbound tourists in Shanghai are analyzed by ensemble empirical mode decomposition. The results show that: first, the total number of inbound tourists and the number of foreign tourists mainly change within 3 or 6 months, while that of Hong Kong, Macao and Taiwan fluctuates between high and low frequency. Second, the main cyclical fluctuations and no significant trend of the source countries. The fluctuation period of Japan, Thailand, Britain, France and Germany is 3 months; Macau is 3, 6, 12, 60, 180 months; Singapore is 3, 6, 180 months. Third, there is a clear trend and cycle fluctuations as a supplement to the source countries. The fluctuation periods in Hong Kong are 3, 6, 90 and 180 months; In Taiwan, Canada and Russia it is 3 , 6 months; In Indonesia, the United States, Italy and New Zealand it is 3, 6 and 12 months; In Malaysia it is 3, 180 months; In South Korea it is 3 ,45 months; In Australia it's four or seven months. Taiwan, Canada, Russia and New Zealand showing the most significant upward trend. From the above research results, specific Suggestions and strategies of market structure competition can be put forward to the inbound tourism industry in Shanghai according to the predicted number of inbound tourists in Shanghai, the structure of the source market and the cyclical fluctuation and trend of the source country.
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