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- Title
Section Passenger Flow Prediction of Urban Rail Transit Based on Multi-source Data and Particle Filter.
- Authors
LIU Zhengqi; WANG Xiaomin
- Abstract
In order to improve the service level of operating organizations, it is very important to reasonably and accurately predict the section passenger flow of urban rail transit. Aiming at the problem that a single data source limits the accuracy of passenger flow forecasting, a particle filter (PF) section passenger flow prediction model based on vehicle weighing data and automatic fare collection system (AFC) data is proposed. Based on the historical operation data of Chengdu Metro Line 9, the correlation between passenger flow of different natures and section passenger flow is analyzed to filter the input features of the model, and the proposed method is verified. The experimental results show that: compared with the single-source PF, the mean absolute error (MAE) of the multi-source PF for the 30 min granularity passenger flow prediction of the morning peak, flat peak and evening peak on holidays (working days) decreased by 28. 541% (60. 969%) and 10. 687% (19. 662%) and 22. 685% (27. 941%) respectively. Compared with the multi-source Kalman filter (KF) and multi-source long short term memory (LSTM) models, the multi-source PF improves the coefficient of determination(R2) for passenger flow forecasting on holidays and working days by at least 21. 599% and 0. 314%. The multi-source PF model has a relatively fast calculation speed and can provide reference for passenger flow prediction of urban rail transit.
- Publication
Railway Standard Design, 2023, Vol 67, Issue 12, p15
- ISSN
1004-2954
- Publication type
Article
- DOI
10.13238/j.issn.1004-2954.202206130006