A Fluid Mechanics-Based Data Flow Model to Estimate VANET Capacity

Jiujun Cheng, Guiyuan Yuan, Mengchu Zhou, Shangce Gao, Cong Liu, Hua Duan

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Accurately estimated data transmission ability is important in operating a vehicular ad-hoc network (VANET), which has limited bandwidth and highly dynamic typology. The mobility behavior of traditional wireless networks is different from VANET's, and existing results on the former are not applicable to VANET directly. Most existing studies on VANET capacity estimation focus on asymptotic descriptions. In them, messages sent and received by vehicle nodes are composed of data packets, and vehicle nodes can move along roads only. In this paper, a modeling and calculation approach for accurate VANET capacity is proposed. We transfer vehicle nodes to data packets and then abstract data packets that can move along roads into data flow in virtual pipelines. Then, we derive a fluid mechanics-based data flow model and propose capacity calculation equations. According to network scale, network capacity is divided into following three stages: linear growth, maintenance, and decline. This paper demonstrates that the data flow model-based capacity is consistent with that of simulation results.

Original languageEnglish (US)
Article number8738878
Pages (from-to)2603-2614
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number6
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


  • Internet of Vehicles
  • data flow
  • fluid mechanics
  • network capacity


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