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Passenger Flow Forecast of Sanya Airport Based on ARIMA Model

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 902))

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Abstract

By analyzing the passenger flow data of Sanya Airport collected from January 2008 to December 2016, the general trend and seasonal variation regularity of the passenger flow can be found. By constructing and testing the ARIMA forecast model, the results show that the ARIMA model has a good fitting effect on the passenger flow data, and its forecast error is small. Therefore, this model can be applied into the short-term forecast of airport passenger flow, and help to provide the corresponding decision-making basis for the airport operation management.

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Acknowledgments

This research was financially supported by Hainan Provincial Natural Science Foundation of China (618QN258) and A cooperative science project between colleges and local government in Sanya (2014YD52). Thanks to associate professor Xia Liu, correspondent of this paper.

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Authors and Affiliations

  1. Sanya Aviation and Tourism College, Sanya, 572000, Hainan, China

    Yuan-hui Li, Hai-yun Han, Xia Liu & Chao Li

Authors
  1. Yuan-hui Li
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  2. Hai-yun Han
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  3. Xia Liu
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  4. Chao Li
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Corresponding author

Correspondence to Xia Liu .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Xidian University, Xi’an, Shaanxi, China

    Qiguang Miao

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Harbin University of Science and Technology, Harbin, China

    Wei Xie

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Li, Yh., Han, Hy., Liu, X., Li, C. (2018). Passenger Flow Forecast of Sanya Airport Based on ARIMA Model. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_36

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  • DOI: https://doi.org/10.1007/978-981-13-2206-8_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2205-1

  • Online ISBN: 978-981-13-2206-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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