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

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, the authors present a modified car-following model based on the intelligent driver model (IDM). Then, the parameters of the authors' 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, the authors 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.

• Record URL:
  https://doi.org/10.1080/21680566.2020.1728591
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21680566
• Supplemental Notes:
  • © 2020 Hong Kong Society for Transportation Studies Limited 2020. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Li, Tenglong
  • Ngoduy, Dong
  • Hui, Fei
  • Zhao, Xiangmo
• Publication Date: 2020-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 150-165
• Serial:
  • Transportmetrica B: Transport Dynamics
  • Volume: 8
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 2168-0566
  • EISSN: 2168-0582
  • Serial URL: https://www.tandfonline.com/toc/ttrb20/current

Subject/Index Terms

• TRT Terms: Car following; Connected vehicles; Data mining; Microsimulation; Perturbations; Traffic flow; Traffic models; Vehicle to vehicle communications; Velocity
• Identifier Terms: Intelligent Driver Model (IDM)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01759641
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 4 2020 4:45PM