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A Federated Personal Mobility Service in Autonomous Transportation Systems

Author

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  • Weitao Jian

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Kunxu Chen

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Junshu He

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Sifan Wu

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Hongli Li

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Ming Cai

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

Abstract

A personal mobility service (PMS) is developed to support personalized travel options for users in autonomous transportation systems (ATS), based on a macro-system state and micro-user behavior. However, this functionality necessitates processing and transmitting vast amounts of data, raising concerns about user privacy protection during data processing and transmission within the PMS. Furthermore, the PMS must be maintained and perform well, while preserving privacy. Therefore, we propose a novel federated PMS, denoted as a FPMS. Specifically, the FPMS can serve users’ personal mobility needs by facilitating the collaboration between the physical and information domains. Then, a common framework for FPMS architectures, which captures the features of ATSs, is proposed and discussed from both physical and logical perspectives, which include both the logical architecture and physical architecture; and we present the key algorithms for the FPMS, in conjunction with a artificial neural network (ANN). Additionally, in static estimation scenarios, the FPMS demonstrated a similar accuracy for three different models compared to the traditional PMS, while reducing the computing time by approximately 60% and communication resource consumption by approximately 85%.

Suggested Citation

  • Weitao Jian & Kunxu Chen & Junshu He & Sifan Wu & Hongli Li & Ming Cai, 2023. "A Federated Personal Mobility Service in Autonomous Transportation Systems," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2693-:d:1170579
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    References listed on IDEAS

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    1. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    2. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
    3. Lo, Hong K. & Szeto, W. Y., 2002. "A methodology for sustainable traveler information services," Transportation Research Part B: Methodological, Elsevier, vol. 36(2), pages 113-130, February.
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