Short term inbound passenger flow prediction of urban rail transit based on data denoising

Zhijian Wang; Qingyun Li; Jiuzeng Wang; Qiang Zhang; Yaoquan Wei

doi:10.1117/12.3024294

14 February 2024 Short term inbound passenger flow prediction of urban rail transit based on data denoising

Zhijian Wang, Qingyun Li, Jiuzeng Wang, Qiang Zhang, Yaoquan Wei

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301823 (2024) https://doi.org/10.1117/12.3024294
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

In order to improve the accuracy of short-term traffic flow prediction, a combined prediction model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Seagull Optimization Algorithm optimized Long-Short Term Memory neural network (SOA-LSTM) was proposed. Firstly, CEEMDAN is used to reduce the interference of data noise, decompose the original passenger flow sequence into multiple characteristic components and residual, and optimize the hyperparameters of the LSTM neural network using SOA. Each feature component is predicted, and the prediction results of each IMFs are summed to obtain the short-term entrance and exit passenger flow prediction results of each station. Extracting four consecutive weeks inbound passenger flow data of Beijing's urban rail transit network in 2019, and validated and compared the proposed short-term passenger flow prediction model. The experimental results demonstrate that compared with the transfer passenger flow prediction results of LSTM, ARIMARBF, LSTM-BP, and SOA-LSTM models, the proposed CEEMDAN-SOA-LSTM model has lower RMSE, MAE, and MAPE error values than other models, with R2 increased by 1.63%, 1.2%, 1.18%, and 0.72%, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhijian Wang, Qingyun Li, Jiuzeng Wang, Qiang Zhang, and Yaoquan Wei "Short term inbound passenger flow prediction of urban rail transit based on data denoising", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301823 (14 February 2024); https://doi.org/10.1117/12.3024294

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