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Taxi Demand Method Based on SCSSA-CNN-BiLSTM

Author

Listed:
  • Dudu Guo

    (School of Transportation Engineering, Xinjiang University, Urumqi 830017, China
    Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China)

  • Miao Sun

    (School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China)

  • Qingqing Wang

    (School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, China)

  • Jinquan Zhang

    (Xinjiang Hualing Logistics & Distribution Co., Urumqi 830000, China)

Abstract

The randomness of passengers’ travel and the blindness of empty drivers seeking passengers can lead to a serious imbalance in the spatio-temporal distribution of taxi supply and demand. In order to realize the accurate prediction of taxi demand, promote a balance between taxi supply and demand, and respond to the requirements of the sustainable development of urban transportation, a travel demand prediction model based on Sparrow Search Algorithm incorporating sine-cosine and Cauchy variants (SCSSA), Convolutional Neural Network (CNN), and Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The key factors affecting travel demand are identified by constructing a set of influencing factors for feature correlation analysis. In order to overcome the overfitting or underfitting phenomenon caused by the improper parameter configuration of the CNN-BiLSTM model, the SCSSA algorithm is utilized to optimize the model. By fine tuning the model parameters, the algorithm enhanced the model’s adaptability to dataset characteristics and improved the accuracy of the prediction results. Compared with CNN, LSTM, CNN- LSTM, CNN-BiLSTM, and SSA-CNN-BiLSTM models, the Root Mean Square Error is decreased by 10.77 on average.

Suggested Citation

  • Dudu Guo & Miao Sun & Qingqing Wang & Jinquan Zhang, 2024. "Taxi Demand Method Based on SCSSA-CNN-BiLSTM," Sustainability, MDPI, vol. 16(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7879-:d:1474735
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    References listed on IDEAS

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    1. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).
    2. Baiping Chen & Wei Li, 2020. "Multitime Resolution Hierarchical Attention-Based Recurrent Highway Networks for Taxi Demand Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, August.
    3. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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