E3S Web Conf.
Volume 214, 20202020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|Number of page(s)||5|
|Section||Big Data Analysis Application and Energy Consumption Research|
|Published online||07 December 2020|
Research on flow patterns of tourists in scenic spots based on Data Mining
School of economics and management, Beijing Jiaotong University, Beijing, China
For the fixed tourist routes in the scenic spot, the longer the journey is, the slower the speed is, and the easier the congestion is. This study is an exploratory study. In this paper, Yudaokou grassland forest scenic area, a nature reserve crossed by national No.1 scenic road, is selected as the research object. Based on the point- axis gradual diffusion theory, the mobile app is used to record the travel process of tourists on the same tourist route in the same scenic area, so as to calculate the travel speed of tourists on different road sections, and then predict the future congestion of the scenic area, the smaller the speed, the greater the probability of congestion; the greater the speed, the less time, the smaller the probability of congestion. Finally, the paper discusses the significance and theoretical contribution of the study on the two aspects of tourists’ moving behavior and moving mode in scenic spots.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.