Abstract
Correctly predicting the passenger flow of an air route is crucial for the construction and development of an airport. Based on the passenger flow data of Sanya Airport from 2008 to 2016, ARMA Model, Grey Prediction GM (1, 1) Model and ARMA-improved Regression Model were adopted for data fitting. Upon verification, the average absolute percentage error of such three models was 4.19%, 4.20% and 1.97% respectively with high prediction precision. As a result, the passenger flow at Sanya Airport is predicted to reach 20 million within two years.
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Liu, X., Huang, X., Chen, L., Qiu, Z., Chen, Mr. (2017). Prediction for Passenger Flow at the Airport Based on Different Models. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_3
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DOI: https://doi.org/10.1007/978-981-10-6442-5_3
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