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A multistep forecasting method for online car-hailing demand based on wavelet decomposition and deep Gaussian process regression

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Abstract

The main objective of this paper is to develop a high-precision forecasting method that can forecast the probability distribution of the demand value of ride hailing. Due to the influence of various complex factors, the demand time series of ride-hailing generates noise and thus affects the accuracy of the forecast. Forecastings of uncertainty can provide a valuable reference for vehicle scheduling decisions. In the present study, the application of wavelet-DGPR models to forecast the demand time series of ride hailing was investigated. The effectiveness of the model was verified by using the demand data of ride-hailing in Hangzhou. The results show that wavelet decomposition can reduce the difficulty of forecasting; DGPR can obtain a probability distribution of demand forecast values for uncertainty forecasting. Wavelet-DGPR has better forecasting accuracy, stability, and robustness than typical methods.

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Data availability

If necessary, please obtain the dataset from Didi Chuxing Technology Co., Ltd.

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Acknowledgements

We acknowledge Didi Chuxing Technology Co., Ltd for providing the ride-hailing dataset.

Funding

This study was funded by the National Natural Science Foundation of China (Grant No. 71971013, 71871003) and the Fundamental Research Funds for the Central Universities (YWF-20-BJ-J-943). The study was also sponsored by the Graduate Student Education & Development Foundation of Beihang University.

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Authors and Affiliations

  1. School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China

    Wenbing Chang, Ruowen Li, Yu Fu, Yiyong Xiao & Shenghan Zhou

Authors
  1. Wenbing Chang
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  2. Ruowen Li
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  3. Yu Fu
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  4. Yiyong Xiao
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  5. Shenghan Zhou
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Contributions

Conceptualization: WC. Data curation: SZ. Formal analysis: RL and YF. Funding acquisition: SZ and YX. Investigation: YF. Methodology: RL and WC. Project administration: SZ. Supervision: WC. Validation: RL and YF. Visualization: RL and WC. Writing—original draft: WC. Writing—review and editing: SZ and YX. Resources: YX. Supervision: SZ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shenghan Zhou.

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Chang, W., Li, R., Fu, Y. et al. A multistep forecasting method for online car-hailing demand based on wavelet decomposition and deep Gaussian process regression. J Supercomput 79, 3412–3436 (2023). https://doi.org/10.1007/s11227-022-04773-0

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  • DOI: https://doi.org/10.1007/s11227-022-04773-0

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