Carsharing is ana lternative to urban mobility that has been widely adopted recently. This service presents three main business models: two of these models base their services on stations while the remainder, the free-floating service, is free of fixed stations. Despite the notable advantages of carsharing, this service is prone to several problems, such as fleet imbalance due to the variance of the daily demand in large urban centers. Forecasting the demand for the service is a key task to deal with this issue. In this sense, in this work, we analyze the use of well-known techniques to forecast a carsharing service demand. More in deep, we evaluate the use of the Long Short-Term Memory (LSTM) and Prophet techniques to predict the demand of three real carsharing services. Moreover, we also evaluate seven state-of-the-art forecasting models on a given free-floating carsharing service, highlighting the potentials of each technique. In addition to historical carsharing service data, we have also used climatic series to enhance the forecasting. Indeed, the results of our analysis have shown that the addition of meteorological data improved the models’ performance. In this case, the mean absolute error of LSTM may fall by half, when using the climate data. When considering the free-floating carsharing service, and prediction for the short-term (i.e., 12 hours), the boosting algorithms (e.g. XGBoost, Catboost, and LightGBM) present superior performance, with less than 20% of mean absolute error when compared to the next best-ranked model (Prophet). On the other hand, Prophet performed better for predictions conducted on long-term periods.

中文翻译：

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使用单变量和多变量模型预测汽车共享服务需求

汽车共享是最近被广泛采用的城市交通的替代方案。该服务提供了三种主要的商业模式：其中两种模式的服务基于电台，而其余的自由浮动服务则没有固定电台。尽管汽车共享具有显着的优势，但这项服务容易出现一些问题，例如由于大城市中心日常需求的差异而导致的车队不平衡。预测对服务的需求是处理这个问题的关键任务。从这个意义上说，在这项工作中，我们分析了使用众所周知的技术来预测汽车共享服务需求。更深入地讲，我们评估了使用长短期记忆 (LSTM) 和 Prophet 技术来预测三种真实汽车共享服务的需求。而且，我们还在给定的自由浮动汽车共享服务上评估了七种最先进的预测模型，突出了每种技术的潜力。除了历史汽车共享服务数据，我们还使用了气候序列来增强预测。事实上，我们的分析结果表明，添加气象数据提高了模型的性能。在这种情况下，使用气候数据时，LSTM 的平均绝对误差可能会下降一半。在考虑自由浮动的汽车共享服务，以及对短期（即 12 小时）的预测时，Boosting 算法（例如 XGBoost、Catboost 和 LightGBM）表现出卓越的性能，相比之下，平均绝对误差小于 20%到下一个排名最高的模型（先知）。另一方面，