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It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k\u2010nearest neighbors (kNN), and deep learning models specifically long short\u2010time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN\u2010LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing\u2019s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN\u2010LSTM outperformed other methods to predict the future car usage. 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