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Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM

Author

Listed:
  • Yulong Pei

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Songmin Ran

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Wanjiao Wang

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Chuntong Dong

    (School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China)

Abstract

The prediction of the bus passenger flow is crucial for efficient resource allocation, frequency setting, and route optimization in bus transit systems. However, it remains challenging for a single model to simultaneously capture the time-series data of the bus passenger flow with periodicity, correlation, and nonlinearity. Aiming at the complex volatility possessed by the time-series data of the bus passenger flow, a new hybrid-strategy bus-passenger-flow prediction model based on wavelet packet decomposition, an attention mechanism, and bidirectional long–short-term memory is proposed to improve the accuracy of bus-passenger-flow prediction. The differences between this study and the existing studies are as follows: Firstly, this model combines decomposition strategies and deep learning. Wavelet packet decomposition can decompose the original data into a series of smoother data components, allowing the model to be more adequate in capturing the temporal characteristics of passenger-flow data. And the model can consider the information after the predicted moment via backward computation. In addition, the model is equipped with the ability to focus on important features by incorporating an attention mechanism to minimize the interference of irrelevant information. Bus-passenger-flow prediction experiments are conducted using the Harbin bus-passenger-flow dataset as an example. The experimental results show that the model proposed in this paper can obtain more accurate bus-passenger-flow prediction results than the five baseline models can obtain.

Suggested Citation

  • Yulong Pei & Songmin Ran & Wanjiao Wang & Chuntong Dong, 2023. "Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14889-:d:1260205
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    References listed on IDEAS

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    1. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Leong, Waiyan & Goh, Karen & Hess, Stephane & Murphy, Paul, 2016. "Improving bus service reliability: The Singapore experience," Research in Transportation Economics, Elsevier, vol. 59(C), pages 40-49.
    3. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    4. Muhammad Ashraf Javid & Nazam Ali & Syed Arif Hussain Shah & Muhammad Abdullah, 2021. "Travelers’ Attitudes Toward Mobile Application–Based Public Transport Services in Lahore," SAGE Open, , vol. 11(1), pages 21582440209, January.
    5. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    6. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
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