九华山风景区旅游资源评价及管理研究
发布时间：2019-06-13 11:19
【摘要】：近年来,随着人民生活水平的提升,旅游业得到了空前的发展。旅游产业是服务型产业,作为景区管理人员必须及时的了解游客的数量和需求,才能为景区制定好长期的旅游规划。因此,如何准确预测景区旅游人数和评价景区当前旅游资源是影响景区未来发展的两个重要课题。本文归纳了旅游管理的研究现状,然后具体以九华山风景区为例,对当前景区的旅游现状进行评价,预测了未来几年的旅游人数,并针对评价结果解决了主要问题。本文的主要工作如下:1.本节对九华山风景区的旅游资源进行评价,首先确定九华山风景区旅游影响因子,运用层次分析法构建了一个包含1个目标层、3个准则层和11个内容层的评价指标体系框架,采取问卷调查的形式,对九华山风景区旅游资源进行评价并分析结果。2.游客数量预测是旅游发展规划的重要内容,本节以九华山风景区近十年的游客数为基础,运用BP神经网络和GM)1,1(模型分别对九华山风景区旅游人数进行预测。实验结果表明,BP神经网络对游客人数预测较为准确,并以此预测未来五年内九华山风景区旅游人数。3.根据旅游资源评价的结果,目前景区待存在的最主要问题是节假日的交通流量问题,本节提出了一种基于网络最大流理论的旅游流量控制模型,通过控制各条线路上的游客人数,使得游客不再感到拥挤,并使得景区总接待人数达到最大。
[Abstract]:In recent years, with the improvement of people's living standards, tourism has been unprecedented development. Tourism industry is a service-oriented industry. As a scenic spot manager, he must understand the number and demand of tourists in time in order to make a good long-term tourism plan for the scenic spot. Therefore, how to accurately predict the number of scenic spots and evaluate the current tourism resources are two important issues that affect the future development of scenic spots. This paper summarizes the research status of tourism management, and then takes Jiuhua Mountain Scenic spot as an example to evaluate the current tourism situation of scenic spots, predict the number of tourists in the next few years, and solve the main problems according to the evaluation results. The main work of this paper is as follows: 1. This section evaluates the tourism resources of Jiuhua Mountain Scenic spot, first determines the tourism impact factors of Jiuhua Mountain Scenic spot, constructs an evaluation index system framework including 1 target layer, 3 criterion layer and 11 content layer by AHP, and evaluates and analyzes the tourism resources of Jiuhua Mountain Scenic spot in the form of questionnaire. 2. The prediction of tourist number is an important part of tourism development planning. based on the number of tourists in Jiuhua Mountain Scenic spot in recent ten years, this section uses BP neural network and GM) 1 (model to predict the number of tourists in Jiuhua Mountain Scenic spot, respectively). The experimental results show that BP neural network is more accurate in predicting the number of tourists, and can predict the number of tourists in Jiuhua Mountain Scenic spot in the next five years. According to the results of tourism resources evaluation, the most important problem of scenic spots is the traffic flow problem of holidays. This section proposes a tourism flow control model based on the network maximum flow theory. By controlling the number of tourists on each route, tourists no longer feel crowded, and the total reception number of scenic spots reaches the maximum.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F592.7
[Abstract]:In recent years, with the improvement of people's living standards, tourism has been unprecedented development. Tourism industry is a service-oriented industry. As a scenic spot manager, he must understand the number and demand of tourists in time in order to make a good long-term tourism plan for the scenic spot. Therefore, how to accurately predict the number of scenic spots and evaluate the current tourism resources are two important issues that affect the future development of scenic spots. This paper summarizes the research status of tourism management, and then takes Jiuhua Mountain Scenic spot as an example to evaluate the current tourism situation of scenic spots, predict the number of tourists in the next few years, and solve the main problems according to the evaluation results. The main work of this paper is as follows: 1. This section evaluates the tourism resources of Jiuhua Mountain Scenic spot, first determines the tourism impact factors of Jiuhua Mountain Scenic spot, constructs an evaluation index system framework including 1 target layer, 3 criterion layer and 11 content layer by AHP, and evaluates and analyzes the tourism resources of Jiuhua Mountain Scenic spot in the form of questionnaire. 2. The prediction of tourist number is an important part of tourism development planning. based on the number of tourists in Jiuhua Mountain Scenic spot in recent ten years, this section uses BP neural network and GM) 1 (model to predict the number of tourists in Jiuhua Mountain Scenic spot, respectively). The experimental results show that BP neural network is more accurate in predicting the number of tourists, and can predict the number of tourists in Jiuhua Mountain Scenic spot in the next five years. According to the results of tourism resources evaluation, the most important problem of scenic spots is the traffic flow problem of holidays. This section proposes a tourism flow control model based on the network maximum flow theory. By controlling the number of tourists on each route, tourists no longer feel crowded, and the total reception number of scenic spots reaches the maximum.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F592.7
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