An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

Jiao, Feng; Huang, Lei; Song, Rongjia; Huang, Haifeng

doi:10.3390/s21175950

Cited by 40 publications

(13 citation statements)

References 40 publications

(63 reference statements)

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“…Among them, seasonal and trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) 23 using robust locally weighted regression as a smoothing method has been widely used in time series preprocessing 24 . Jiao et al 25 combined it with a neural network model to obtain STL-LSTM for bus passenger flow prediction during the COVID-19 epidemic.…”

Section: Introductionmentioning

confidence: 99%

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Jiang

2023

Sci Rep

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A model with high accuracy and strong generalization performance is conducive to preventing serious pollution incidents and improving the decision-making ability of urban planning. This paper proposes a new neural network structure based on seasonal–trend decomposition using locally weighted scatterplot smoothing (Loess) (STL) and a dependency matrix attention mechanism (DMAttention) based on cosine similarity to predict the concentration of air pollutants. This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. In this paper, the long short-term memory network (LSTM) and the gated recurrent unit network (GRU) are set as the baseline models to design experiments. At the same time, to test the generalization performance of the model, short-term forecasts in hours were performed using PM2.5, PM10, SO2, NO2, CO, and O3 data. The experimental results show that the model proposed in this paper is superior to the comparison model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE values of the 6 kinds of pollutants are 6.800%, 10.492%, 9.900%, 6.299%, 4.178%, and 7.304%, respectively. Compared with the baseline LSTM and GRU models, the average reduction is 49.111% and 43.212%, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Jiang

2023

Sci Rep

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“…LSTM network is often used in time series prediction. Many researchers combine it with different methods to predict subway passenger flow and have achieved good results [5][6][7].…”

Section: Passenger Flow Forecastmentioning

confidence: 99%

Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model

Zeng

Xue

et al. 2022

Applied Sciences

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The metro transportation system will have emergency passenger flow for various reasons, resulting in passenger flow congestion, affecting efficiency and risks. In this paper, the LSTM network is applied to predict the normal passenger flow and emergency passenger flow of metro transportation based on transfer learning to solve the imbalanced data set problem when the amount of emergency samples is too small. The results show that under normal and emergency conditions, the average prediction error is less than 5%, which provides an alarm for the operating company to take preventive measures in advance. Compared with the strategy without transfer learning, it proves that the strategy proposed in this paper has advantages in predicting emergency conditions.

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“…According to the defined parameters and algorithms, LSTM neural network adds three gates structure to control the state of memory cells in each neuron: the input gate, the output gate and the forget gate (Fig. 2), all of which are controlled by the Sigmoid unit (0,1) [35].…”

Section: Lstm Modelmentioning

confidence: 99%

Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China

et al. 2022

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Background This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model and multivariate Long Short-Term Memory Neural Network (LSTM) models. Methods Disability adjusted life year (DALY) was used to evaluate the disease burden of occupational pneumoconiosis. ARIMA model, DNN model and multivariate LSTM model were used to establish prediction model. Three performance evaluation metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the prediction effects of the three models. Results From 1990 to 2021, there were 10,694 cases of pneumoconiosis patients in Tianjin, resulting in a total of 112,725.52 person-years of DALY. During this period, the annual DALY showed a fluctuating trend, but it had a strong correlation with the number of pneumoconiosis patients, the average age of onset, the average age of receiving dust and the gross industrial product, and had a significant nonlinear relationship with them. The comparison of prediction results showed that the performance of multivariate LSTM model and DNN model is much better than that of traditional ARIMA model. Compared with the DNN model, the multivariate LSTM model performed better in the training set, showing lower RMES (42.30 vs. 380.96), MAE (29.53 vs. 231.20) and MAPE (1.63% vs. 2.93%), but performed less stable than the DNN on the test set, showing slightly higher RMSE (1309.14 vs. 656.44), MAE (886.98 vs. 594.47) and MAPE (36.86% vs. 22.43%). Conclusion The machine learning techniques of DNN and LSTM are an innovative method to accurately and efficiently predict the burden of pneumoconiosis with the simplest data. It has great application prospects in the monitoring and early warning system of occupational disease burden.

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An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

Cited by 40 publications

References 40 publications

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition

Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model

Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China

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