Short-Term Urban Rail Passenger Flow Prediction using TCN-LSTM Model
DOI:
https://doi.org/10.54097/p83p5860Keywords:
Short-time passenger flow; forecast; TCN-LSTM model; prediction accuracy.Abstract
Predicting short-term passenger flow in urban rail transit systems is crucial for efficient operation and management. The TCN-LSTM model, which combines Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) networks, offers a robust solution for this task. TCNs provide an expansive receptive field, enabling the model to capture long-range dependencies and trends in passenger flow data. Meanwhile, LSTMs are adept at handling sequential data, refining the temporal features extracted by the TCN layers to account for short-term dependencies and fluctuations. This hybrid approach leverages the strengths of both architectures, resulting in a model that can accurately predict passenger volumes, accommodating regular traffic patterns and sudden changes in rider behavior. The implementation of the TCN-LSTM model in urban rail transit systems leads to optimized train schedules, improved service efficiency, and enhanced passenger experiences. By enabling more dynamic and responsive transit management, this model plays a pivotal role in addressing the challenges of modern urban transportation.
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[1] Zeng Lu, Li Zino, Yang Jie, et al. Research on short-term passenger flow forecasting method of urban rail Transit based on CEEMDAN-IPSO-LSTM. Journal of railway science and engineering, 2023, 20(9): 3273-3286.
[2] Wu J, et al. A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit.UrbanRailTransit, 2023, 9: 323-351.
[3] Zhang Wanning, Zheng Mingming, Liu Yan. Short-term passenger flow prediction of urban rail transit based on PSO-VMD-LSTM model. Journal of Shandong Jiaotong University, 2019, 31(04): 43-50.
[4] Zhao Mingwei, Zhang Wensheng, Wang Kewen, et al. Short-term passenger flow prediction of urban rail transit based on EMD-PSO-LSTM combined model. Railway transportation and economy, 2022, 44(7): 110-118.
[5] Shi Minlian, Liu Zhigang, Hu Hua, et al. Short-term passenger flow prediction of Urban Rail Transit based on PCA-LSTM Model. Intelligent Computer and Applications, 2020, 10(03): 155-159.
[6] Wang Jinfeng, Sun Lian-Ying, Zhang Tian, et al. Short-term passenger flow prediction of urban rail transit based on K-LSTM-ecm model. Manufacturing Automation, 2002, 44(05): 103-107+133.
[7] Meng Ge, Hao Xiaopei, Zhang Junfeng, et al. Passenger flow prediction model of Urban rail Transit based on PSO-FSVR. Urban Rail Transit Research, 2023, 26(10): 43-48.
[8] Ma C, et al. Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism. Physica A: Statistical Mechanics and its Applications, 2024.
[9] Cao Yang, Sun Ya, Li Lin, et al. Research on short-time inbound passenger flow prediction of urban rail transit based on CNN-LSTM. Traffic and Transportation, 2018, 40(02): 94-99.
[10] Xia L. Research on forecasting method of regional electricity consumption based on ARIMA model and regression analysis. Nanjing University of Science and Technology, 2013.
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