Short-term travel demand forecasting throughout a city is crucial for passengers,drivers and the on-demand ride service platform,which could reduce waiting time and fuel consumption.In this paper,we propose a novel stacked bidirectional long short-term memory neural network(SBi-LSTMs)that can forecast short-term travel demand in each area of a city based on historical demand data and other relevant information.The proposed model is evaluated on the real-world data provided by China's largest on-demand ride platform(DiDi Chuxing).The experimental results show that the SBi-LSTM outperforms other benchmark algorithms in predicting large-scale travel demand,such as ANN,RNN and LSTM.In addition,we analyzed the effects of different parameters on performance and training time.
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