Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network

Qiuxia Sun; Guoxiang Chu; Qing Li; Xiuyan Jia

doi:10.1117/12.2652773

21 November 2022 Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network

Qiuxia Sun, Guoxiang Chu, Qing Li, Xiuyan Jia

Author Affiliations +

Proceedings Volume 12340, International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022); 123400X (2022) https://doi.org/10.1117/12.2652773
Event: International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), 2022, Lanzhou, China

Abstract

In order to reduce the risks caused by congestion to scenic spot management and tourist safety, a dynamic Bayesian network model based on K-means++ clustering is proposed to realize the prediction of tourist transfer volume between scenic spots. Firstly, the K-means++ method is used to cluster the tourist transfer volume between scenic spots, we select the best number of clustering by the elbow rule, and the grade interval is determined by clustering results. Secondly, we consider the passenger transfer volume and tourist flow as the nodes of the dynamic Bayesian network, which can estimate the probability of tourist transfer from the upstream scenic spots to the target scenic spot, and the tourist volume of the target scenic spot is predicted. Finally, the confusion matrix is used to verify the validity of the proposed model. The case study shows: 1.) The prediction accuracy of the model can reach about 96%, which indicates that the model is suitable for tourist flow prediction. 2.) Compared to ARIMA, SVR, K-means + BN, and K-means + DBN, the proposed model has better prediction accuracy. 3.) The Bayesian network model outperforms deep learning models in interpretability.

Citation Download Citation

Qiuxia Sun, Guoxiang Chu, Qing Li, and Xiuyan Jia "Prediction model of passenger transfer volume between scenic spots based on clustering and dynamic Bayesian network", Proc. SPIE 12340, International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022), 123400X (21 November 2022); https://doi.org/10.1117/12.2652773

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KEYWORDS

Data modeling

Performance modeling

Global Positioning System

Mathematical modeling

Roads

Analytical research

Probability theory

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