Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks

Chen, Dewang; Zhang, Jianhua; Jiang, Shanshan

doi:10.1109/access.2020.2995044

Cited by 81 publications

(35 citation statements)

References 33 publications

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“…The LSTM neural network is an improved algorithm with neurons that can keep memory in their channels to effectively mitigate the vanishing gradient problem [35]. The key of the LSTM neural network is the cell state which can add or remove information to the cell state through the gates [36]. An LSTM network consists of four interactive cells including an input gate, an output gate, a forget gate, and an internal unit.…”

Section: B Long Short-term Memorymentioning

confidence: 99%

Use of Machine Learning for Deception Detection From Spectral and Cepstral Features of Speech Signals

Fernandes

Ullah

2021

IEEE Access

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In this research, four unique nonlinear speech features are extracted and analyzed to study the dissimilarity pattern between when the speaker is being deceitful and truthful based on how human speech is perceived. The speaker was under stress in a police interrogation where two ground truth and two deceitful responses were recorded during three different times of the day. Using the audio recordings from all three sessions, the cepstral features and spectral energy features are extracted. Cepstral features are the Mel frequency cepstrum coefficient, from where the delta cepstrum and the time-difference cepstrum features are developed. On the other hand, the spectral energy features are the energy of Bark band energy from where the delta energy and the time-difference energy features are developed. The Levenberg-Marquardt classification method and the long short-term memory classification method are then applied to evaluate the accuracy of detecting deception based on the nine unique training and testing combinations of the three different sessions and their extracted cepstrum and spectral energy features. In addition, the principal component analysis is applied to reduce the dimensionality from the extracted features for further improvement. The projected principal components of the four types of features showed improved accuracy in order to distinguish between truthful and deceptive speech pattern. After incorporating with principal component analysis, the long short-term memory classification method with time-difference spectral energy feature shows the highest recognition rate compared to Levenberg-Marquardt algorithm with other cepstral and spectral features.

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Section: B Long Short-term Memorymentioning

confidence: 99%

Use of Machine Learning for Deception Detection From Spectral and Cepstral Features of Speech Signals

Fernandes

Ullah

2021

IEEE Access

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“…ey took Beijing ring road as an example to demonstrate the feasibility of the model in identifying congestion sources. Chen et al [14] proposed a hybrid algorithm that combines the addition mode of seasonal-trend decomposition based on loess and the LSTM neural network (STL-LSTM) to mitigate the influences of irregular fluctuation and improve the performance of short-term subway ridership prediction. Ai et al [15] used Conv-LSTM to solve the problem of airport delay prediction in the network structure and verify the effectiveness of the model.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction

Chen

Liu

et al. 2021

Journal of Advanced Transportation

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When urban rail transit is faced with a large number of commuter passengers during peak periods, passengers are often waiting for the next train because the subway is running at full load, which causes delays to the overall travel time of passengers. The calculation and prediction of the congestion delay in subway stations can guide the operation department and passengers to make better planning and selection. In this paper, we use a new method based on deep learning technology to evaluate the congestion delay of subway stations. Firstly, we use automatic fare collection (AFC) system data to evaluate the congestion delays of stations. Then, we use a convolutional long short-term memory (Conv-LSTM) network to extract spatial and temporal characteristics to solve the short-term prediction problem of the subway congestion delay in the network structure. The spatiotemporal variables include inbound passenger flow, outbound passenger flow, number of passengers delayed, and average delay time. As a spatiotemporal sequence, the input and prediction targets are both spatiotemporal three-dimensional tensors in the end-to-end training model. The effectiveness of the method is verified by a case study of the Chongqing Rail Transit. Experimental results show that Conv-LSTM is better than the benchmark models in capturing spatial and temporal correlation.

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“…The decomposed components are forecasted separately, and then, these predicted results are summed as the final outcomes. The widely used time series decomposition methods include: wavelet decomposition (WD) [ 25 , 33 ], empirical mode decomposition (EMD) [ 2 , 26 , 34 ], Seasonal and Trend Decomposition Using Loess (STL) [ 35 , 36 ], singular spectrum analysis (SSA) [ 37 , 38 , 39 ], and so on. Sun et al [ 25 ] and Liu et al [ 33 ] employed the WD approach to decompose the original passenger flow into several high-frequency and low-frequency sequences, and then, these sequences were forecasted based on least squares SVR by Sun et al [ 25 ] and extreme learning machine (ELM) by Liu et al [ 33 ], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Then, they developed the Echo State Network (ESN) to forecast each decomposed series. Chen et al [ 36 ] also employed STL to decompose the daily metro ridership, and LSTM was used in the prediction stage. As for the SSA method, to the best of our knowledge, this method has never been introduced to an analysis passenger flow to date, although this method was devolved for traffic flow prediction.…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Zhou

Wang

Zhao

2020

Sensors

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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.

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Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks

Cited by 81 publications

References 33 publications

Use of Machine Learning for Deception Detection From Spectral and Cepstral Features of Speech Signals

Use of Machine Learning for Deception Detection From Spectral and Cepstral Features of Speech Signals

A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction

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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