School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.
National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China.
PLoS One. 2019 Sep 11;14(9):e0222365. doi: 10.1371/journal.pone.0222365. eCollection 2019.
Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.
短期地铁客流量预测是智能交通系统（ITS）的重要组成部分，可以应用于优化车站客流组织，并为地铁客流预警和系统管理提供数据支持。长短时记忆（LSTM）神经网络最近在自然语言处理（NLP）领域取得了显著的进展，因为它们非常适合从经验中学习来预测时间序列。为此，我们提出了一种基于经验模态分解（EMD）的长短时记忆（LSTM）神经网络模型，用于预测短期地铁进站客流量。EMD 算法将原始的顺序客流量分解为几个固有模态函数（IMFs）和一个残差。通过特征选择，可以获得与原始数据强相关的选定 IMF。选定的 IMF 和原始数据被集成到 LSTM 神经网络的输入中，并开发了单个 LSTM 预测模型和 EMD-LSTM 混合预测模型。最后，从成都地铁采集了历史真实的自动售检票（AFC）数据来验证所提出的 EMD-LSTM 预测模型的有效性。结果表明，所提出的 EMD-LSTM 混合预测模型优于 LSTM、ARIMA 和 BPN 模型。
