A Quantum Bayesian approach for bike sharing demand prediction

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.