Volume 45 Issue 1
Jan.  2019
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XING Zhiwei, HE Chuan, LUO Qian, et al. Terminal building short-term passenger flow forecast based on two-tier K-nearest neighbor algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(1): 26-34. doi: 10.13700/j.bh.1001-5965.2018.0259(in Chinese)
Citation: XING Zhiwei, HE Chuan, LUO Qian, et al. Terminal building short-term passenger flow forecast based on two-tier K-nearest neighbor algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(1): 26-34. doi: 10.13700/j.bh.1001-5965.2018.0259(in Chinese)

Terminal building short-term passenger flow forecast based on two-tier K-nearest neighbor algorithm

doi: 10.13700/j.bh.1001-5965.2018.0259
Funds:

National Natural Science Foundation of China U1533203

Safety Capacity Constructing Funds Project of CAAC FDSA0032

Science and Technology Support Program of Sichuan Province 2016GZ0068

Strategic Emerging Product R & D Subsidy Project of Chengdu 2015-CP01-00158-GX

More Information
  • Corresponding author: LUO Qian, E-mail: luoqian@caacetc.com
  • Received Date: 07 May 2018
  • Accepted Date: 28 Jul 2018
  • Publish Date: 20 Jan 2019
  • Outbound passenger flow of terminal building shows the quasi-periodic variation in a short period of time and also shows complex nonlinear characteristics because of many factors such as flight schedule and weather. In order to accurately predict the short-term passenger flow of terminal building, the flight schedule state pattern matching procedure is added on the basis of the traditional K-nearest neighbor (KNN) algorithm. The flight schedule including multi-dimensional attributes is taken as a feature to select historical similar operation days as forecast reference vectors. The two-tier K-nearest neighbor model based on terminal building short-term passenger flow forecast is built. Through instance analysis and comparison with ARIMA model and traditional K-nearest neighbor model, it is proved that two-tier K-nearest neighbor model has smaller prediction error and higher precision, and the model fitting degree increases by 8%-10% compared with traditional K-nearest neighbor model. Thus the model provides a new solution for accurately forecasting terminal building short-term passenger flow.

     

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