Abstract
It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport. Based on the passenger flow data of Sanya Airport from 2008 to 2016, this paper respectively adopted Holt-Winter Seasonal Model, ARMA and linear regression model to predict the passenger flow of Sanya Airport from 2017 to 2018. In order to reduce the prediction error and improve the prediction accuracy at meanwhile, the combinatorial weighted method is adopted to predict the data in a combined manner. Upon verification, this method has been proved to be an effective approach to predict the airport passenger flow.
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Acknowledgments
This research was financially supported by A cooperative science project between colleges and local government in Sanya (2013YD64) and (2014YD52).
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Liu, X., Huang, X., Chen, L., Qiu, Z., Chen, Mr. (2017). Prediction of Passenger Flow at Sanya Airport Based on Combined Methods. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_61
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DOI: https://doi.org/10.1007/978-981-10-6385-5_61
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