Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

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Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017, methodology): “Combining the empirical mode decomposition (EMD) and back-propagation neural network (BPN), Wei and Chen (2012) presented a hybrid EMD-BPN method for short- term passenger flow forecasting.”
SARIMA modelling approach for railway passenger flow forecasting (2016, unknown): “For example, Wei and Chen (2012) developed a hybrid forecasting approach based on a combination of Empirical Mode Decomposition (EMD) and Back-Propagation Neural (BPN) networks to predict short term passenger flow in metro systems.”
LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “Over the past few decades, many data analysis models have been proposed to solve the short-term traffic forecast, including historical average and smoothing [4, 5], statistical and regression methods [6, 7], traffic flow theory-based methods [8, 9] and machine learning techniques [10, 11].”
Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification (2014, unknown): “, 2008), hybrid EMDBPN (empirical mode decomposition-back propagation neural networks) approach (Wei and Chen, 2012), chaos-wavelet analysis-support vector machine approach (Wang and Shi, 2013).”