# Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition

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## Abstract

**:**

## 1. Introduction

#### 1.1. Background

#### 1.2. Purpose and Significance

## 2. Literature Review

- In view of the fact that the EMD method is prone to modal aliasing, EEMD can avoid modal aliasing by stacking different Gaussian white noise with equal amplitude. With the advantages of the memory and forgetting of LSTM, the combination model is applied to the field of metro short-term passenger-flow prediction to ensure the accuracy of short-term prediction.
- The accuracy of EEMD-LSTM prediction in practical application is further explored. By changing the scale of the training set to achieve the effect of dynamic prediction, the feasibility of the model in practice is verified by comparing the static prediction results of dynamic prediction without changing the training set.

## 3. Methodology

#### 3.1. Data Sources

#### 3.2. Ensemble Empirical Mode Decomposition (EEMD)

#### 3.3. Long Short-Term Memory Neural Network (LSTM)

#### 3.4. EEMD-LSTM Model

- The AFC data is pre-processed to obtain the OD time series s(t) between stations.
- EEMD method is used to decompose s(t) to obtain n components of intrinsic mode function (IMF) and residuals.
- The partial autocorrelation function (PACF) is used as an index to calculate the autocorrelation between the components of each eigenfunction and the residual, and the corresponding LSTM time step is determined.
- The function components and residuals of the intrinsic model are divided into training set and test set to predict IMFs and residuals, respectively.
- The predicted daily OD passenger-flow data is obtained by integrating the predicted intrinsic mode function components and residuals.

#### 3.5. Model Building

## 4. Model Validation

#### Results and Analysis of Precision

## 5. Discussion

## 6. Conclusions

- On the basis of the existing LSTM neural network prediction of short-term passenger flow, EEMD is used to decompose the local characteristic signal of the passenger-flow sequence at the entry and exit stations at different time, so as to weaken the interference of sample noise in the accuracy of the prediction model. With the AFC data of the Dalian metro Line 1 and Line 2 used for testing, the prediction error of the EEMD-LSTM model was reduced by 3.625% on average, compared with that of the EMD-LSTM model, indicating that EEMD-LSTM has higher prediction accuracy.
- Starting from the 35-day historical data, the OD value of the next 7 days was predicted. The actual amount of the next day was added to historical data, and then the OD value of next 7 days was predicted again. By analogy, until 42 days is taken as historical data, the prediction accuracy of training samples with different historical data was compared. The results show that the average prediction error of historical samples from the 35-day one to the 42-day one decreases from 12.57% to 10.06%, and shows a trend of further decreases, indicating that the dynamic prediction has higher accuracy than the static prediction method by continuously increasing the scale of the training set.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Historical OD values. (

**a**) Historical OD of commercial–residential station; (

**b**) historical OD of residential–commercial station; (

**c**) historical OD of scenic–residential station; (

**d**) historical OD of residential–scenery station.

**Figure 7.**Partial autocorrelation function of intrinsic mode function components and residual terms. (

**a**)IMF1; (

**b**) IMF2; (

**c**) IMF3; (

**d**)IMF4; (

**e**)IMF5; (

**f**) RES.

**Figure 8.**OD passenger-flow prediction results. (

**a**) OD prediction of commercial–residential station; (

**b**) OD prediction of residential–commercial station; (

**c**) OD prediction of scenic–residential station; (

**d**) OD prediction of residential–scenery station.

Commercial–Residential Station | Residential–Commercial Station | Scenic–Residential Station | Residential–Scenery Station | |||||
---|---|---|---|---|---|---|---|---|

EMD-LSTM | EEMD-LSTM | EMD-LSTM | EEMD-LSTM | EMD-LSTM | EEMD-LSTM | EMD-LSTM | EEMD-LSTM | |

MAPE | 8.434 | 8.158 | 22.418 | 9.082 | 15.195 | 15.124 | 13.375 | 12.556 |

RSME | 69.557 | 58.842 | 138.272 | 56.070 | 20.174 | 20.039 | 16.749 | 16.171 |

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**MDPI and ACS Style**

Cao, Y.; Hou, X.; Chen, N.
Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition. *Sustainability* **2022**, *14*, 8562.
https://doi.org/10.3390/su14148562

**AMA Style**

Cao Y, Hou X, Chen N.
Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition. *Sustainability*. 2022; 14(14):8562.
https://doi.org/10.3390/su14148562

**Chicago/Turabian Style**

Cao, Yi, Xiaolei Hou, and Nan Chen.
2022. "Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition" *Sustainability* 14, no. 14: 8562.
https://doi.org/10.3390/su14148562