A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

doi:10.15837/ijccc.2020.5.3906

A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

2020-08-30

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DOI：10.15837/ijccc.2020.5.3906

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In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments.

A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

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