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Optimizing e-Bike sharing demand prediction and allocation by imparting quantum techniques: QLSR and QAOA with linguistic and logistic factors

algorithms-and-analysis-of-algorithms
computer-networks-and-communications
emerging-technologies
optimization-theory-and-computation
quantum-computing

Abstract

Effective e-bike availability at major transit hubs, such as metro stations, railway stations, malls, airports, or bus terminals, is key to smooth urban mobility. This paper utilizes a Quantum Link State Routing (QLSR) model with Quantum Link State Packet (QLSP) processing to efficiently predict and allocate e-bikes. Optimal demand forecasting and real-time e-bike positioning are achieved by integrating linguistic factors like temperature, humidity, wind speed, and logistic factors such as working day, holiday, weekend, etc., into the model. It uses principles of quantum computing, including quantum superposition and entanglement, to make rapid adaptive routing decisions for the deployment of an e-bike. Implementation findings indicate e-bike accessibility at user-administrable locations has the potential to reduce shortages and increase profitability for bike operators. Additionally, the results demonstrate the effectiveness of routing with quantum mechanics in urban mobility planning.
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