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Title	Long-Term Prediction Of Bikes Availability On Bike-Sharing Stations
ID_Doc	35584
Authors	Cenni D.; Collini E.; Nesi P.; Pantaleo G.; Paoli I.
Year	2021
Published	Proceedings - DMSVIVA 2021: 27th International DMS Conference on Visualization and Visual Languages
DOI	http://dx.doi.org/10.18293/DMSVIVA2021-001
Abstract	Bike-sharing systems have been adopted in many cities as a valid alternative to traditional public transports since they are eco-friendly, prevent traffic congestions, reduce the probability of social contacts which are probable in public means. On the other hand, they also bring some problems which include the irregular distribution of bikes on the stations/racks/areas and the difficulty of knowing in advance their status with a certain degree of confidence, whether there will be available bikes at a specific bike-station at certain time of the day, or a free slot for leaving the rented bike. Therefore, providing predictions can be useful for improving the quality of service. This paper presents a technique to predict the number of available bikes and free bike-slots in bike-sharing stations (which is still the best solution for e-bikes). To this end, a set features and predictive models have been compared to identify the best models and predictors for long-term predictions. The solution and its validation have been performed by using data collected in bike-stations in the cities of Siena and Pisa, in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure. The Gradient Boosting Machine (GBM) offers a robust approach for the implementation of reliable and fast predictions of available bikes in terms of flexibility and robustness with respect to critical cases, producing long-terms predictions in critical conditions (when available bikes are few). © DMSVIVA 2021.All right reserved.
Author Keywords	Available bikes prediction; Bike-sharing; Machine learning; Prediction models; Smart city

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