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dc.contributor.authorHarikrishnakumar, Ramkumar
dc.contributor.authorBorujeni, Sima E.
dc.contributor.authorDand, Alok
dc.contributor.authorNannapaneni, Saideep
dc.identifier.citationHarikrishnakumar, R., Borujeni, S. E., Dand, A., & Nannapaneni, S. (2020). A quantum bayesian approach for bike sharing demand prediction. Paper presented at the Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 2401-2409. doi:10.1109/BigData50022.2020.9378271en_US
dc.descriptionClick on the DOI link to access this conference paper at the publishers website (may not be free).en_US
dc.description.abstractThe bike-sharing system (BSS) aims to provide an alternative mode of public transportation that are being adopted in urban cities. The use of the bike for short-distance travel aids in mitigating traffic congestion problems, reduce carbon emissions, and decrease the risk of overcrowding during a pandemic like COVID-19, thereby satisfying the urban-mobility needs of the residents. The key success of incorporating urban-mobility through BSS lies behind the prediction of bikes by identifying the pick-up and drop-off operations in each station. The main challenge includes the demand prediction for the number of bikes available for pick-up and drop-off during a specific point in time. Quantum Computing has been increasingly gaining popularity for its superior computational performance over similar classical algorithms. In this paper, we will illustrate potential applications of Quantum Bayesian networks, which are quantum-equivalent to classical Bayesian networks for probabilistic bike demand prediction.en_US
dc.relation.ispartofseries2020 IEEE International Conference on Big Data (Big Data);
dc.subjectBayesian networken_US
dc.subjectBike sharing systemen_US
dc.subjectDemand predictionen_US
dc.subjectUrban mobilityen_US
dc.titleA Quantum Bayesian approach for bike sharing demand predictionen_US
dc.typeConference paperen_US
dc.rights.holder©2020 IEEEen_US

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