Intelligent management of bike sharing in smart cities using machine learning and Internet of Things

Abstract

Global ecological requirements are pushing city actors to opt for ecological solutions at all levels, including urban mobility. More sustainable Bike-sharing systems (BSS) have become an indispensable part of the transport offer by world's major metropolis. Like any computerized service system, they generate voluminous and complex data that the use of which is essentially limited to the management and operation of the system. The movements made by system users can provide valuable information on many aspects of urban life including the spatial and temporal dynamics of travel in the city, on the place of the bicycle among other modes of transport, or on the distribution of territorial and social inequalities in geographical space. In this paper, we study the problem of intelligent management of shared bicycle systems. Indeed, the management of these systems faces many optimization problems in its procession. Thus, to improve the BSS user's satisfaction, it's useful to inform the actors/users in this system about the state of bike sharing for a station. For this, we propose an approach that integrates in these systems both the new IoT for smart city technologies and machine learning in order to facilitate the task of management, availability and profitability. In addition, we propose an automatic management system capable of predicting the number of bikes shared per hour, day or month by taking several dynamic parameters. Simulation results carried out on real data from London's bike sharing system demonstrate the effectiveness of the proposed model.