Abstract
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.
Highlights
In the last few years, several studies have focused on the understanding of urban mobility and its applications [1,2,3]
The deeper studies we conduct on the free-floating carsharing system show that the boosting algorithms (e.g.Extreme Gradient Boosting (XGBoost), Catboost, and LightGBM) have superior performance, with less than 20% of mean absolute error, when compared to the best-ranked model (Prophet), for short-term predictions
4.1 ARIMA and Seasonality Auto-Regressive Integrated Moving Average (SARIMA) The Auto-Regressive Integrated Moving Average (ARIMA) and its derivations are widely used in the time-series forecast [2, 14, 15]
Summary
In the last few years, several studies have focused on the understanding of urban mobility and its applications [1,2,3]. The growing attention to urban mobility occurs due to its relationship with –practically– all of the daily tasks, as people labor, education, security, and health These recent studies suggest that the increasing complexity within such tasks requires intelligent solutions for better resource management. The current sharing economy phenomenon, which is an economic model mostly based on a peer-to-peer (P2P) mode, influenced the creation of several solutions for urban mobility This occurs since people can share goods (e.g., vehicles), mainly through. Alencar et al Journal of Internet Services and Applications (2021) 12:4 online platforms [4] In this way, carsharing is an urban mobility solution that is receiving increasing attention from the academic community and industry [1, 2, 5, 6]. In 2015, this kind of service handled more than 1.5 million users and 22 thousand vehicles in circulation in the Americas [3]
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