A Quantum Bayesian approach for bike sharing demand prediction
Harikrishnakumar, Ramkumar Borujeni, Sima E. Dand, Alok Nannapaneni, Saideep
Harikrishnakumar, Ramkumar
Borujeni, Sima E.
Dand, Alok
Nannapaneni, Saideep
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2021-03-19
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Conference paper
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Keywords
Bayesian network,Bike sharing system,Demand prediction,Quantum,Urban mobility
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Citation
Harikrishnakumar, 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/BigData50022.2020.9378271
Abstract
The 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.
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IEEE
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2020 IEEE International Conference on Big Data (Big Data);