The study on forecasting demand for online car-hailing holds substantial implications for both online car-hailing platforms and government agencies responsible for traffic management. This research proposes an enhanced Empirical Mode Decomposition Long-short Term Memory Neural Network (EMD-LSTM) model. EMD technique reduces noise and extracts stable intrinsic mode functions (IMF) from the original time series. Genetic algorithm is deployed to improve the K-Means clustering for determining optimal clusters. These sub time series serve as input for the prediction model, with combined results giving final predictions. Experimental data from Didi includes Haikou’s car-hailing orders from May to October 2017 and Beijing’s from January to May 2020. Results show improved EMD-LSTM reduces instability and captures characteristics better. Compared to unmodified EMD-LSTM, RMSE decreases by 3.50%, 6.81%, and 6.81% for the three datasets, and by 30.97%, 20%, and 9.24% respectively compared to single LSTM model.


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    Titel :

    Enhanced forecasting of online car-hailing demand using an improved empirical mode decomposition with long short-term memory neural network


    Weitere Titelangaben:

    J. LIU ET AL.
    TRANSPORTATION LETTERS


    Beteiligte:
    Liu, Jiaming (Autor:in) / Tang, Xiaoya (Autor:in) / Liu, Haibin (Autor:in)

    Erschienen in:

    Transportation Letters ; 16 , 10 ; 1389-1405


    Erscheinungsdatum :

    25.11.2024


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch