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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine
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Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

机译：基于奇异谱分析和AdaBoost加权加权极限学习机的地铁换乘站客流预测

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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. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting.
机译：地铁系统在城市公共交通中发挥着重要作用，而客流预测对于帮助运营商建立智能交通系统（ITS）至关重要。预测结果可以为旅行者的出行决策和经理的地铁运营提供必要的信息。为了研究客流的内部特征并以更少的培训时间进行更准确的预测，采用了一种新颖的模型（即SSA-AWELM），奇异频谱分析（SSA）和AdaBoost加权极限学习机（AWELM）的组合），是本文提出的。 SSA的开发目的是将原始数据分解为趋势，周期性和残差三个部分。开发AWELM是为了拼命地预测每个组件。将三个预测结果相加作为最终结果。在实验中，数据集是从中国杭州地铁的自动收费系统（AFC）中收集的。我们提取了三周的客流，以进行多步预测测试和比较分析。结果表明，提出的SSA-AWELM模型可以减少预测误差和训练时间。尤其是，与流行的深度学习模型长短期记忆（LSTM）神经网络相比，SSA-AWELM平均将测试错误减少了22％，并节省了84％的时间。它表明，SSA-AWELM是一种有前途的客流预测方法。

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