Tourist hot spots prediction model based on optimized neural network algorithm

Xiaofei Huang (), Vishal Jagota (), Einer Espinoza-Muñoz () and Judith Flores-Albornoz ()
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Xiaofei Huang: Chengdu Polytechnic
Vishal Jagota: Madanapalle Institute of Technology and Science
Einer Espinoza-Muñoz: Universidad Nacional Santiago Antúnez de Mayolo
Judith Flores-Albornoz: Universidad Nacional Santiago Antúnez de Mayolo

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 7, 63-71

Abstract: Abstract With the increase in the tourism industry, the experience of the tourist is changed dramatically. Mainly, there are two types of sustainability in the tourism industry. One is for the sustainable destination environment and other is for the sustainable tourists’ experience. For the tourists’ sightseeing recommendation, an invulnerable system is needed to the site based on the current ground conditions. The tourist volume prediction of tourist attractions in tourism research has always been an interesting topic and one of the difficult problems faced by the tourism field. The RBF neural network algorithm was utilized to the parameters optimization and the popular tourist spots prediction model was established to study and predict popular tourist spots, which was compared with the traditional prediction model. The results show that the particle swarm optimization neural network can better track the change rules of popular tourist attractions, and the prediction accuracy of popular tourist attractions is obviously better than that of the traditional model. The tourist attractions prediction efficiency is also higher, which can meet the requirements of online prediction of popular tourist attractions. The training time and prediction time of the prediction model of popular tourist attractions are reduced which speeds up the modeling efficiency of the popular tourist attractions prediction and allows online prediction of popular tourist attractions.

Keywords: Tourism management system; Hot spots; Neural network; Nonlinear change characteristics; Prediction model; Tourist attractions (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01226-4

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