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Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

  • Review
  • Open access
  • Published: 25 October 2022
  • Volume 65, article number 211301, (2022)
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Science China Information Sciences Aims and scope Submit manuscript
Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey
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  • Qimei Cui1,2,
  • Xingxing Hu3,
  • Wei Ni4,
  • Xiaofeng Tao1,2,
  • Ping Zhang1,2,
  • Tao Chen5,
  • Kwang-Cheng Chen6 &
  • …
  • Martin Haenggi7 

  • 1363 Accesses

  • 12 Citations

  • 39 Altmetric

  • 5 Mentions

  • Explore all metrics

Abstract

With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

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Acknowledgements

The work was supported by Joint Funds for Regional Innovation and Development of National Natural Science Foundation of China (Grant No. U21A20449), National Natural Science Foundation of China (Grant No. 61971066), National Youth Top-notch Talent Support Program, and Major Key Project of PCL (Grant No. PCL2021A15).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qimei Cui, Xiaofeng Tao & Ping Zhang

  2. Department of Broadband Communication, Peng Cheng Laboratory, Shenzhen, 518055, China

    Qimei Cui, Xiaofeng Tao & Ping Zhang

  3. China Mobile Research Institute, Beijing, 100053, China

    Xingxing Hu

  4. Digital Productivity and Services Flagship, Commonwealth Scientific and Industrial Research Organization (CSIRO), Sydney, N.S.W., 2122, Australia

    Wei Ni

  5. VTT Technical Research Center of Finland, Espoo, FI-02044 VTT, Finland

    Tao Chen

  6. Department of Electrical Engineering, University of South Florida, Tampa, FL, 33620, USA

    Kwang-Cheng Chen

  7. Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA

    Martin Haenggi

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  1. Qimei Cui
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  2. Xingxing Hu
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  3. Wei Ni
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  4. Xiaofeng Tao
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  5. Ping Zhang
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  6. Tao Chen
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  7. Kwang-Cheng Chen
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  8. Martin Haenggi
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Corresponding author

Correspondence to Qimei Cui.

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Cui, Q., Hu, X., Ni, W. et al. Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey. Sci. China Inf. Sci. 65, 211301 (2022). https://doi.org/10.1007/s11432-021-3487-x

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  • Received: 13 September 2021

  • Revised: 09 February 2022

  • Accepted: 21 April 2022

  • Published: 25 October 2022

  • DOI: https://doi.org/10.1007/s11432-021-3487-x

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Keywords

  • vehicular mobility pattern
  • Internet-of-Vehicles (IoV)
  • traffic flow
  • spatial point process
  • trajectory prediction
  • machine learning
  • deep learning
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