Mobile Networks and Applications

, Volume 23, Issue 6, pp 1487–1495 | Cite as

Big Data Aided Vehicular Network Feature Analysis and Mobility Models Design

  • Ruoxi Sun
  • Jing Ye
  • Ke Tang
  • Kai Zhang
  • Xin Zhang
  • Yong Ren


Vehicular networks play a pivotal role in intelligent transportation system (ITS) and smart city (SC) construction, especially with the coming of 5G. Mobility models are crucial parts of vehicular network, especially for routing policy evaluation as well as traffic flow management. The big data aided vehicle mobility analysis and design attract researchers a lot with the acceleration of big data technology. Besides, complex network theory reveals the intrinsic temporal and spatial characteristics, considering the dynamic feature of vehicular network. In the following content, a big GPS dataset in Beijing, and its complex features verification are introduced. Some novel vehicle and location collaborative mobility schemes are proposed relying on the GPS dataset. We evaluate their performance in terms of complex features, such as duration distribution, interval time distribution and temporal and spatial characteristics. This paper elaborates upon mobility design and graph analysis of vehicular networks.


Big data Vehicular network Complex network Mobility models 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ruoxi Sun
    • 1
  • Jing Ye
    • 2
  • Ke Tang
    • 3
  • Kai Zhang
    • 4
  • Xin Zhang
    • 4
  • Yong Ren
    • 4
  1. 1.Department of Electronic EngineeringTsinghua University, China Transport Telecommunications and Information CenterBeijingChina
  2. 2.Faculty of EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  4. 4.Department of Electronic EngineeringTsinghua UniversityBeijingChina

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