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Space-time distribution model of visitor flow in tourism culture construction via back propagation neural network model

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Abstract

In order to study the spatial and temporal distribution of visitor flow in the construction of tourism culture, this article first studies and analyzes the space-time behavior of tourists, and divides the environment of the scenic spot into three parts: entrance, exit, and stop. Then, based on the three parts, the time and space distribution data of tourists are collected from five aspects: the arrival probability distribution of tourists, the probability of transition between tourist attractions, the distribution of attraction time, the moving time between attractions, and the area of scenic spots. The number of visitors in each attraction, and by fitting the curve, the probability distribution of the tourist time of each attraction is obtained. Finally, a neural network–based prediction model of the space-time distribution of tourists is established. The collected data is brought into the neural network. By comparing the predicted values with the actual values, the model has high prediction accuracy and can be used to predict the spatial and temporal distribution of tourists.

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Authors and Affiliations

  1. Tourism and Urban-Rural Planning College, Chengdu University of Technology, Chengdu, 610059, China

    Xian Li

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Li, X. Space-time distribution model of visitor flow in tourism culture construction via back propagation neural network model. Pers Ubiquit Comput 24, 223–235 (2020). https://doi.org/10.1007/s00779-019-01342-w

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  • DOI: https://doi.org/10.1007/s00779-019-01342-w

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