旅游地阶段预测模型构建及实证研究

Construction of Tourism Destination State Forecasting Model and Case Study

本文通过引入旅游地游客量变化的"速度"和"加速度"概念体系,将定量描述旅游地生命周期的Logistic曲线模型展开为级数形式,推导并构建了基于旅游学理论的旅游地阶段预测模型。文中对旅游地在不同演化阶段运用该模型的条件及相应的数据处理方法进行了详细阐述,创建了一种对旅游地生命周期演化阶段定量划分的新方法,更重要的是该模型能够对旅游地不同演化周期游客量最大值及到达时间进行预测。该模型计算过程简便,且避免了Logistic曲线模型中参数设置的主观性缺陷,增加了预测的准确性。文中通过实证研究,验证了所构建的预测模型与实例演化过程相符。这为旅游地游客量预测提供了一种简便和准确的新方法,在理论研究与方法上取得了一定的进展。

For any prediction model of research theoretical analysis, method selection and data and construction, the functioning factors include processing. Nowadays the existing problems of tourist destination prediction research are that most prediction models are based on case study, and few prediction have followed the evolution disciplines of tourist destination. Therefore, these predictions lack basic theoretical support in tourism and do not have a general directive function. Besides, Butler （1980） put forward Tourist Area Life Cycle Theory, which are agreed by most scholars, They believe that it is a theoretical abstraction for tourist destination evolution process, and generally regard it as an appropriate expression for the real evolution process of tourist destination. Based on that, in order to describe tourist destination evolution process in quantity, Logistic Curve Prediction Model is introduced to the tourist destination evolution process research. However, to use this model to predict tourists amount of tourist destination, maximum visiting number must be calculated first in order to derive constants ＂h＂ and ＂a＂, and then regression calculation procedure can be carried out. However, the calculation process is not only complicated, but it can also be subjective. Meanwhile, in Logistic Curve Prediction Model, there is no time limitation for tourist destination to reach maximum tourists number, which can not reflect actual evolution situation of tourist destination. In this paper, we employ the concepts of tourist growth ＂speed＂ and ＂acceleration＂ and deploys Logistic Curve Prediction Model Formula which is a quantitative description of the tourist destination life cycle in the series format. Then, tourist Forecast Model is established on the basis of tourist subject theory. The conditions of using the Model and corresponding date-processing methods in tourist destination different evolution stages are explained in details, and a new method for quantitative partition to tourist destination life cycle stages is developed. Furthermore, we can use the Model to forecast maximum tourists number and the time. At the same time, every stage of the Model formula gets the corresponding regression equation counterpart, which provids regression equations for tourism theory. Finally, after the empirical studies, author verified the consistent of the Model with the example of the evolution process. This Tourism Forecast Model provides a simple and accurate new method for forecast and is a progress in theory research.