Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization
This paper proposes a prediction method based on chaos theory and an improved empirical-modal-decomposition particle-swarm-optimization long short-term-memory (EMD-PSO-LSTM)-combined optimization process for passenger flow data with high nonlinearity and dynamic space-time dependence, using EMD to p...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/9524966 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548014437171200 |
|---|---|
| author | Lixin Zhao Hui Jin Xintong Zou Xiao Liu |
| author_facet | Lixin Zhao Hui Jin Xintong Zou Xiao Liu |
| author_sort | Lixin Zhao |
| collection | DOAJ |
| description | This paper proposes a prediction method based on chaos theory and an improved empirical-modal-decomposition particle-swarm-optimization long short-term-memory (EMD-PSO-LSTM)-combined optimization process for passenger flow data with high nonlinearity and dynamic space-time dependence, using EMD to process the original passenger flow data and generate several eigenmodal functions (IMFs) and residuals with different characteristic scales. Based on the chaos theory, each component of the PSO algorithm was improved by introducing an inertia factor to facilitate the adjustment of its search capability to improve optimization. Each subsequence of the phase-space reconstruction was built into an improved PSO-LSTM prediction model, and the output of each prediction model was summed to determine the final output. Experimental studies were performed using data from the North Railway Station of Chengdu Rail Transit, and the results showed that the proposed model can generate better prediction results. The proposed model obtained root mean square error (RMSE) and mean absolute error (MAE) of 16.0908 and 11.3704, respectively. Compared with the LSTM, the improved PSO-LSTM, the improved EMD-PSO-LSTM, and the model proposed in this paper improved the RMSE values by 25.53%, 29.97%, and 58.76%, respectively, and the MAE values by 30.41%, 40.13%, and 63.08%, respectively, of the prediction results. |
| format | Article |
| id | doaj-art-cbf0bd9f95e747de8cbfc582ac24c734 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-cbf0bd9f95e747de8cbfc582ac24c7342025-02-03T06:42:44ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/9524966Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined OptimizationLixin Zhao0Hui Jin1Xintong Zou2Xiao Liu3Liaoning University of Technology of ChinaLiaoning University of Technology of ChinaLiaoning University of Technology of ChinaNorth China University of TechnologyThis paper proposes a prediction method based on chaos theory and an improved empirical-modal-decomposition particle-swarm-optimization long short-term-memory (EMD-PSO-LSTM)-combined optimization process for passenger flow data with high nonlinearity and dynamic space-time dependence, using EMD to process the original passenger flow data and generate several eigenmodal functions (IMFs) and residuals with different characteristic scales. Based on the chaos theory, each component of the PSO algorithm was improved by introducing an inertia factor to facilitate the adjustment of its search capability to improve optimization. Each subsequence of the phase-space reconstruction was built into an improved PSO-LSTM prediction model, and the output of each prediction model was summed to determine the final output. Experimental studies were performed using data from the North Railway Station of Chengdu Rail Transit, and the results showed that the proposed model can generate better prediction results. The proposed model obtained root mean square error (RMSE) and mean absolute error (MAE) of 16.0908 and 11.3704, respectively. Compared with the LSTM, the improved PSO-LSTM, the improved EMD-PSO-LSTM, and the model proposed in this paper improved the RMSE values by 25.53%, 29.97%, and 58.76%, respectively, and the MAE values by 30.41%, 40.13%, and 63.08%, respectively, of the prediction results.http://dx.doi.org/10.1155/2023/9524966 |
| spellingShingle | Lixin Zhao Hui Jin Xintong Zou Xiao Liu Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization Journal of Advanced Transportation |
| title | Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization |
| title_full | Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization |
| title_fullStr | Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization |
| title_full_unstemmed | Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization |
| title_short | Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization |
| title_sort | short term passenger flow forecasting for rail transit considering chaos theory and improved emd pso lstm combined optimization |
| url | http://dx.doi.org/10.1155/2023/9524966 |
| work_keys_str_mv | AT lixinzhao shorttermpassengerflowforecastingforrailtransitconsideringchaostheoryandimprovedemdpsolstmcombinedoptimization AT huijin shorttermpassengerflowforecastingforrailtransitconsideringchaostheoryandimprovedemdpsolstmcombinedoptimization AT xintongzou shorttermpassengerflowforecastingforrailtransitconsideringchaostheoryandimprovedemdpsolstmcombinedoptimization AT xiaoliu shorttermpassengerflowforecastingforrailtransitconsideringchaostheoryandimprovedemdpsolstmcombinedoptimization |