Abstract
At present, with the emerging of the independent travel, the tourist flow is equipped with stronger nonlinear feature. According to lots of researches, the multiple-source data integration could realize the higher tourist flow prediction accuracy than the prediction only based on the single source data. Targeting to regional passengers’ multiple joints for travel space-time behavior as feature, the paper proposed the multiple-source data integration and explores to applies the situation awareness to the regional tourism flow prediction so as to formulate the neural network model based on the intelligent neuron component. Then, the author also utilizes this model to forecast the regional tourist flow as well as presses ahead the empirical researches by taking the tourist attractions of Hainan Province as example. By virtue of the experimental simulation, it analyzes the advantages of the prediction model than the prediction based on single source data.
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Acknowledgments
During the visiting in Networked Information Systems Laboratory Waseda University. This work was supported by Hainan Natural Science Foundation of china under Grant No. 617172.
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Wu, Y., Fu, T., Wang, T.M. (2020). The Research of Regional Tourist Flow Situation Assessment Based on Time Variant and Multi-source Data. In: Park, J., Yang, L., Jeong, YS., Hao, F. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2019 2019. Lecture Notes in Electrical Engineering, vol 590. Springer, Singapore. https://doi.org/10.1007/978-981-32-9244-4_2
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DOI: https://doi.org/10.1007/978-981-32-9244-4_2
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