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.

中文翻译：

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基于卷积长短期记忆神经网络的预测模型

近年来，在线汽车租赁服务的市场需求已急剧增长。为了满足日常出行需求，重要的是准确地预测在线乘车服务的供应和需求，并根据预测的供应和需求之间的差距进行主动调度。结合卷积神经网络（CNN）和长短期记忆（LSTM）的优点，提出了一种新颖的在线汽车供需预测模型。提出的模型称为卷积LSTM（C-LSTM）。接下来，处理有关在线汽车叫车的原始数据，并提取影响供需预测的关键特征。之后，在训练过程中通过AdaBound算法优化了C-LSTM。最后，