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Open Access

A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
School of Information Engineering, Ganzhou Key Laboratory of Cloud Computing and Big Data Research, Gannan University of Science andTechnology, Ganzhou 341000, China
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Abstract

Currently, the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning issue. Accurately identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult, ultimately preventing the achievement of the most optimal solution. This paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive reasoning. Intuitive reasoning aims to enhance performance in scenarios with poor system robustness, complex tasks, and diverse goals. A deep learning model is constructed, trained to extract scene features, and combined with expert knowledge, then transformed into a quantum annealable form. The final strategy is obtained using a D-wave quantum computer with quantum tunneling effect, which helps in finding optimal solutions by jumping out of local suboptimal solutions. Based on 400000 real data, four algorithms are compared: minimum-cost flow, sequential markov decision process, hot-dot strategy, and driver-prefer strategy. The average total revenue increases by about 10% and vehicle utilization by about 15% in various scenarios. In summary, the proposed architecture effectively solves the e-hailing reposition problem, offering new directions for robust artificial intelligence in big data decision problems.

Keywords

intuitive reasoningreinforcement learningquantum annealing

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Intelligent and Converged Networks
Volume 5 Issue 4,
December 2024
Pages 317-335
DOI: 10.23919/ICN.2024.0020
Cite this article:
Wang C, Shi Y, Wang S. A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning. Intelligent and Converged Networks, 2024, 5(4): 317-335. https://doi.org/10.23919/ICN.2024.0020

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Received: 14 March 2024
Revised: 07 April 2024
Accepted: 03 July 2024
Published: 31 December 2024
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
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