A car-following model to assess the impact of V2V messages on traffic dynamics

Tenglong Li, Dong Ngoduy, Fei Hui, Xiangmo Zhao

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Connected vehicles (CVs) are considered to have the potential to significantly improve traffic flow stability. Although several studies have been devoted to modelling car-following behaviour in a connected environment, most model formulations are based on assumptions without empirical observations. Therefore, this paper utilizes data from field experiments to explore the dynamics of CVs. Data mining analysis shows that the driver is more responsive to velocity differences with safety messages. According to the data analysis results, we present a modified car-following model based on the intelligent driver model (IDM). Then, the parameters of our modified IDM are calibrated. It is shown that the modified IDM is able to reproduce the observed experimental data better than the original IDM. Next, we conduct a linear stability analysis of the modified IDM to explore the properties of the model. Finally, simulation experiments are conducted to verify the theoretical analysis.

Original languageEnglish
Pages (from-to)150-165
Number of pages16
JournalTransportmetrica B: Transport Dynamics
Volume8
Issue number1
DOIs
Publication statusPublished - 16 Feb 2020
Externally publishedYes

Keywords

  • Car-following model
  • data mining analysis
  • linear stability
  • microscopic traffic simulation
  • vehicle-to-vehicle communications

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