Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives

Automated vehicles (AVs) are widely considered to play a crucial role in future transportation systems because of their speculated capabilities in improving road safety, saving energy consumption, reducing vehicle emissions, increasing road capacity, and stabilizing traffic. To materialize these widely expected potentials of AVs, a sound understanding of AVs’ impacts on traffic flow is essential. Not surprisingly, despite the relatively short history of AVs, there have been numerous studies in the literature focusing on understanding and modeling various aspects of AV-involved traffic flow and significant progresses have already been made. To understand the recent development and ultimately inspire new research ideas on this important topic, this survey systematically and comprehensively reviews the existing AV-involved traffic flow models with different levels of details, and examines the relationship among the design of AV-based driving strategies, the management of transportation systems, and the resulting traffic dynamics. The pros and cons of the existing models and approaches are critically discussed, and future research directions are also provided.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103101
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21001224
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Haiyang Yu et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yu, Haiyang
  • Jiang, Rui
  • He, Zhengbing
  • Zheng, Zuoduo
  • Li, Li
  • Liu, Runkun
  • Chen, Xiqun
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 103101
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 127
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Alternatives analysis; Autonomous vehicles; Highway capacity; Macroscopic traffic flow; Mesoscopic traffic flow; Microscopic traffic flow; Traffic flow
• Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01774056
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 11 2021 2:37PM