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A fast matrix autoregression algorithm based on Tucker decomposition for online prediction of nonlinear real-time taxi-hailing demand without pre-training

Author

Listed:
  • Xu, Zhihao
  • Lv, Zhiqiang
  • Chu, Benjia
  • Li, Jianbo

Abstract

Online prediction of real-time taxi-hailing demand generally provides better real-time decision support for passengers and taxi drivers compared with offline prediction. Current studies focused on using deep spatial-temporal models to predict complex nonlinear taxi-hailing demand. However, whether these models can be used for online prediction of real-time taxi-hailing demand through online training or offline pre-training is hardly discussed. Generally, deep models are not lightweight enough for online training, and pre-training these models requires some time and computational resources. Therefore, a lightweight Fast Matrix Autoregression algorithm based on Tucker Decomposition (FMAR-TD) is proposed for online real-time training and prediction of nonlinear taxi-hailing demand without pre-training. The experimental results show that FMAR-TD achieves millisecond-level online prediction of real-time taxi-hailing demand. Compared with baselines, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of FMAR-TD marginally increase by 2.51 % and 2.56 %, while the computation time (sum of training time and prediction time) significantly reduces by 86.16 %. Open-source link: https://github.com/qdu318/FMAR-TD.

Suggested Citation

  • Xu, Zhihao & Lv, Zhiqiang & Chu, Benjia & Li, Jianbo, 2024. "A fast matrix autoregression algorithm based on Tucker decomposition for online prediction of nonlinear real-time taxi-hailing demand without pre-training," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    • Handle: RePEc:eee:chsofr:v:189:y:2024:i:p1:s0960077924012128
    DOI: 10.1016/j.chaos.2024.115660
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    References listed on IDEAS

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    1. Di Wang & Yao Zheng & Heng Lian & Guodong Li, 2022. "High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1338-1356, September.
    2. Xu, Zhihao & Lv, Zhiqiang & Chu, Benjia & Li, Jianbo, 2024. "A Fast Spatial-temporal Information Compression algorithm for online real-time forecasting of traffic flow with complex nonlinear patterns," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
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