山岳型旅游风景区日客流量预测模型研究
发布时间：2018-08-24 08:59
【摘要】：随着中国社会环境的和谐发展和人民生活水平的大幅度提高,我国的旅游业也展现出了它蓬勃的生命力。然而,旅游业的快速发展却提高了旅游景区管理者科学决策的难度,尤其是山岳型风景区,因其独特的地貌特征,在资源调度和资源保护方面的问题更加突出、更加难以解决。为了从根源降低山岳型景区协调管理的难度,建立山岳型日客流量预测模型,从而为景区管理者提供科学的决策依据,让山岳型景区的旅游环境能够在科学布局和区别决策中保持健康蓬勃的生命力。论文的主要研究如下:(1)对国内外学者的文献研究进行梳理,以黄山景区六年的日客流量数据特点为研究对象,分析山岳型风景区日客流量的影响因素和变化特点。依据黄山景区日客流量的变化特点,将其分为平常日客流量和节假日客流量,从而可以根据不同时间节点的客流量数据特点,分别构建不同的日客流量预测模型。(2)针对黄山风景区平常日客流量数据似线性的特点,构建基于灰色系统的平常日预测模型。在研究中,引用GM(1,1)模型作为基础预测模型。在研究中,针对基础模型存在的不足依次做出以下优化:以新陈代谢优化提高基础灰色预测模型预测结果的可靠性;以平滑指数优化样本序列,提高样本序列的规律;以残差修正优化样本序列,提高预测模型的精度。最后通过几种优化方法的组合,建立基于灰色系统组合优化的平常日客流量预测模型。实验证明,基于灰色预测组合优化的平常日客流量预测模型符合预测精度的要求,且预测效果优于神经网络模型。(3)针对黄山景区节假日客流量数据周期性强的特点,构建基于神经网络的节假日预测模型。选取黄山景区节假日客流量为模型样本,通过对比不同的影响因素和参数值的预测模型精度,确定模型的构建要素。实验证明,基于神经网络的预测模型比基于灰色系统的预测模型更适合节假日预测。
[Abstract]:With the harmonious development of Chinese social environment and the improvement of people's living standard, the tourism industry of our country has also shown its vigorous vitality. However, the rapid development of tourism has increased the difficulty of scientific decision-making of scenic spot managers, especially the mountain scenic spot, because of its unique geomorphological characteristics, the problems in resource scheduling and resource protection are more prominent and more difficult to solve. In order to reduce the difficulty of coordinated management of mountain scenic spots from the root causes, establish the forecasting model of daily passenger flow of mountain type, and provide scientific decision basis for scenic spot managers. The tourism environment of mountain scenic spot can maintain healthy and vigorous vitality in scientific layout and differentiation decision. The main research of this paper is as follows: (1) combing the literature research of domestic and foreign scholars, taking the characteristics of daily passenger flow data of six years in Huangshan scenic spot as the research object, analyzing the influencing factors and changing characteristics of the daily passenger flow of mountain scenic spot. According to the characteristics of daily passenger flow in Huangshan scenic area, it can be divided into normal daily passenger flow and holiday passenger flow, which can be based on the characteristics of passenger flow data of different time nodes. Different daily passenger flow forecasting models are constructed respectively. (2) according to the characteristic that the daily passenger flow data of Huangshan Scenic spot appear linear, the daily forecasting model based on grey system is constructed. In the study, the GM (1 + 1) model is used as the basic prediction model. In the research, the following optimization is made according to the shortcomings of the basic model: to improve the reliability of the prediction results of the basic grey prediction model by metabolic optimization, to optimize the sample sequence by smoothing index, and to improve the regularity of the sample sequence. The precision of the prediction model is improved by modifying the sample sequence with residual error. Finally, through the combination of several optimization methods, a daily passenger flow forecasting model based on grey system combination optimization is established. The experiment proves that the daily passenger flow forecasting model based on grey forecast combination optimization meets the requirement of forecasting precision and the forecasting effect is better than that of neural network model. (3) aiming at the strong periodicity of holiday passenger flow data in Huangshan scenic spot, The holiday prediction model based on neural network is constructed. Huangshan scenic spot holiday passenger flow as model sample, by comparing different factors and parameters of the prediction model accuracy, determine the building elements of the model. Experimental results show that the prediction model based on neural network is more suitable for holiday prediction than that based on grey system.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F592.7
[Abstract]:With the harmonious development of Chinese social environment and the improvement of people's living standard, the tourism industry of our country has also shown its vigorous vitality. However, the rapid development of tourism has increased the difficulty of scientific decision-making of scenic spot managers, especially the mountain scenic spot, because of its unique geomorphological characteristics, the problems in resource scheduling and resource protection are more prominent and more difficult to solve. In order to reduce the difficulty of coordinated management of mountain scenic spots from the root causes, establish the forecasting model of daily passenger flow of mountain type, and provide scientific decision basis for scenic spot managers. The tourism environment of mountain scenic spot can maintain healthy and vigorous vitality in scientific layout and differentiation decision. The main research of this paper is as follows: (1) combing the literature research of domestic and foreign scholars, taking the characteristics of daily passenger flow data of six years in Huangshan scenic spot as the research object, analyzing the influencing factors and changing characteristics of the daily passenger flow of mountain scenic spot. According to the characteristics of daily passenger flow in Huangshan scenic area, it can be divided into normal daily passenger flow and holiday passenger flow, which can be based on the characteristics of passenger flow data of different time nodes. Different daily passenger flow forecasting models are constructed respectively. (2) according to the characteristic that the daily passenger flow data of Huangshan Scenic spot appear linear, the daily forecasting model based on grey system is constructed. In the study, the GM (1 + 1) model is used as the basic prediction model. In the research, the following optimization is made according to the shortcomings of the basic model: to improve the reliability of the prediction results of the basic grey prediction model by metabolic optimization, to optimize the sample sequence by smoothing index, and to improve the regularity of the sample sequence. The precision of the prediction model is improved by modifying the sample sequence with residual error. Finally, through the combination of several optimization methods, a daily passenger flow forecasting model based on grey system combination optimization is established. The experiment proves that the daily passenger flow forecasting model based on grey forecast combination optimization meets the requirement of forecasting precision and the forecasting effect is better than that of neural network model. (3) aiming at the strong periodicity of holiday passenger flow data in Huangshan scenic spot, The holiday prediction model based on neural network is constructed. Huangshan scenic spot holiday passenger flow as model sample, by comparing different factors and parameters of the prediction model accuracy, determine the building elements of the model. Experimental results show that the prediction model based on neural network is more suitable for holiday prediction than that based on grey system.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F592.7
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