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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.

Car-sharing is an alternative to urban mobility that has been widely adopted. However, this approach is prone to several problems, such as fleet imbalance, due to the variance of the daily demand in large urban centers. In this work, we apply two time series techniques, namely, Long Short-Term Memory (LSTM) and Prophet, to infer the demand for three real car-sharing services. We also apply several state-of-the-art models on free-floating data in order to get a better understanding of what works best for this type of data. In addition to historical data, we also use climatic attributes in LSTM applications. As a result, the addition of meteorological data improved the model’s performance, especially on Evo: an average Mean Absolute Error (MAE) of approximately 61.13 travels was obtained with the demand data on Evo, while MAE equals 32.72 travels was observed when adding the climatic data, the other datasets also improved but none other improved this much. For the free-floating data test, we got the Boosting Algorithms (XGBoost, Catboost, and LightGBM) got the best performance short term, the worst one has an improvement of around 22% of MAE over the next best-ranked (Prophet). Meanwhile in the long term Prophet got the best MAE result, around 22.5% better than the second-best (LSTM).

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