The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B4-2020
24 Aug 2020
 | 24 Aug 2020


J. F. Toro, D. Carrion, M. A. Brovelli, and M. Percoco

Keywords: bike-sharing, urban mobility, spatial analysis, data visualization

Abstract. Bike Sharing Systems (BSS) are growing worldwide for the social and environmental benefits that they can provide. Thanks to the increasing popularity of the BSS and the availability of monitoring technologies, there is a continuous production of data that can help to understand bike usage and to improve its design and management. This study aims at exploring BSS users’ mobility patterns habits and the demand for the service. To reach this scope, the available data have been preprocessed in order to allow data mining and data visualization with open source tools based on Python. The study case regards the BikeMi BSS of the City of Milan between June 2015 to December 2018. The suggested approach proceeded, first, with the categorization of the user typology based on the frequency of use of the service; at a second stage, the influence of the day typology on the use of the service has been explored; third, the spatial and temporal patterns of the BSS use among the stations has been analysed; fourth, the influence of meteorological conditions on the use of the service has been considered; at last, the clustering of the stations with similar bikes use activity through K-Means has been performed. As expected, it was observed that the service is extensively used for commuting to work-related activities. Regular users compose a large part of the BSS community making use of the service mostly during weekdays. In addition, it was noted that only 'strong' meteorological conditions can impact the use of the service. Both the identification of the demand for the service and of the external factors that can affect its use support the clustering activities, allowing for the elimination of not relevant information and facilitating the interpretation of the obtained clusters.