sensors Article Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine Wei Zhou 1,2,3 , Wei Wang 1,2,3,* and De Zhao 1,2,3 1 School of Transportation, Southeast University, Nanjing 211189, China;
[email protected] (W.Z.);
[email protected] (D.Z.) 2 Jiangsu Key Laboratory of Urban ITS, Nanjing 211189, China 3 Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Nanjing 211189, China * Correspondence:
[email protected] Received: 19 May 2020; Accepted: 19 June 2020; Published: 23 June 2020 Abstract: The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis.