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A Hybrid Model for Ride-hailing Service Demand Forecasting

The accurate prediction of the ride-hailing service demand is important for the improvement of transport capability. Due to the nonlinearity and instability of the time series, the prediction accuracy is not ideal. In this paper, we proposes a hybrid model that combines Discrete Wavelet Transform (DWT), Autoregressive Integrated Moving Average (ARIMA), and Radial Basis Function Neural Network (RBFNN) together, which takes advantage of the good fitting ability of the ARIMA model and the strong nonlinear relation mapping ability of the RBFNN model, and use DWT to decompose the time series in order to isolate the influence of each resolution coefficient to a certain extent. The experimental results show that the proposed model has higher prediction accuracy than the single model such as ARIMA, RBFNN and other hybrid models such as ARIMA-DWT, RBFNN-DWT, RBFNN-ARIMA, SVR-ARIMA-DWT, LSTM-ARIMA-DWT.