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

Qimei Cui (Corresponding Author), Xingxing Hu, Wei Ni, Xiaofeng Tao, Ping Zhang, Tao Chen, Kwang Cheng Chen, Martin Haenggi

Research output: Contribution to journalReview Articlepeer-review

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.

Original languageEnglish
Article number211301
Number of pages42
JournalScience China Information Sciences
Volume65
Issue number11
DOIs
Publication statusPublished - 25 Oct 2022
MoE publication typeA2 Review article in a scientific journal

Keywords

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

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