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Smart Recommendations for Renting Bikes in Bike-Sharing Systems

dc.contributor.authorBillhardt, Holger
dc.contributor.authorFernández, Alberto
dc.contributor.authorOssowski, Sascha
dc.date.accessioned2023-12-26T16:54:12Z
dc.date.available2023-12-26T16:54:12Z
dc.date.issued2021-10-16
dc.identifier.citationBillhardt,H.;Fernández, A.; Ossowski, S. Smart Recommendations for Renting Bikes in Bike-Sharing Systems. Appl. Sci. 2021,11,9654.es
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/10115/27868
dc.descriptionThis work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities, co-funded by EU FEDER Funds, through grant RTI2018-095390-B-C33 (MCIU/AEI/FEDER, UE).es
dc.description.abstractVehicle-sharing systems—such as bike-, car-, or motorcycle-sharing systems—have become increasingly popular in big cities in recent years. On the one hand, they provide a cheaper and environmentally friendlier means of transportation than private cars, and on the other hand, they satisfy the individual mobility demands of citizens better than traditional public transport systems. One of their advantages in this regard is their availability, e.g., the possibility of taking (or leaving) a vehicle almost anywhere in a city. This availability obviously depends on different strategic and operational management decisions and policies, such as the dimension of the fleet or the (re)distribution of vehicles. Agglutination problems—where, due to usage patterns, available vehicles are concentrated in certain areas, whereas no vehicles are available in others—are quite common in such systems, and need to be dealt with. Research has been dedicated to this problem, specifying different techniques to reduce imbalanced situations. In this paper, we present and compare strategies for recommending stations to users who wish to rent or return bikes in station-based bike-sharing systems. Our first contribution is a novel recommendation strategy based on queuing theory that recommends stations based on their utility to the user in terms of lower distance and higher probability of finding a bike or slot. Then, we go one step further, defining a strategy that recommends stations by combining the utility of a particular user with the utility of the global system, measured in terms of the improvement in the distribution of bikes and slots with respect to the expected future demand, with the aim of implicitly avoiding or alleviating balancing problems. We present several experiments to evaluate our proposal with real data from the bike sharing system BiciMAD in Madrid.es
dc.language.isoenges
dc.publisherMDPIes
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectbike sharinges
dc.subjectagent-based coordinationes
dc.subjectsmart transportationes
dc.subjectsmart mobilityes
dc.subjectmulti-agent systemses
dc.titleSmart Recommendations for Renting Bikes in Bike-Sharing Systemses
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.3390/app11209654es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses


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Atribución 4.0 InternacionalExcept where otherwise noted, this item's license is described as Atribución 4.0 Internacional