Traffic capacity implications of automated vehicles mixed with regular vehicles

Automated vehicles (AVs) have begun to receive tremendous interest among researchers and decision-makers because of their substantial safety and mobility benefits. Although much research has been reported regarding the implications of Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) technologies for highway capacity, to the authors' knowledge, evaluations of the impacts of AVs are rare. AVs can be divided into two categories, cooperative and autonomous. Cooperative AVs, unlike Autonomous AVs, can communicate with other vehicles and infrastructure, thereby providing better sensing and anticipation of preceding vehicles' actions, which would have an impact on traffic flow characteristics. This paper proposes an analytical framework for quantifying and evaluating the impacts of AVs on the capacities of highway systems. To achieve this goal, the behavior of AVs technologies incorporated on the car-following and lane-merging modules in the traffic micro-simulation model, based on which an estimate of the achievable capacity is derived. To consider the period before AVs account for a majority of the vehicles in traffic networks, the proposed model considers combinations of vehicles with varying market penetration. The results indicate that a maximum lane capacity of 6,450 vph per lane (300% improvement) is achievable if all vehicles are driven in a cooperative automated manner. Regarding the incorporation of autonomous AVs into the traffic stream, the achievable capacity appears highly insensitive to market penetration. The results of this research provide practitioners and decision-makers with knowledge regarding the potential capacity benefits of AVs with respect to market penetration and fleet conversion.

• Record URL:
  https://doi.org/10.1080/15472450.2017.1404680
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
• Supplemental Notes:
  • © 2018 Taylor & Francis Group, LLC 2018. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Olia, Arash
  • Razavi, Saiedeh
  • Abdulhai, Baher
  • Abdelgawad, Hossam
• Publication Date: 2018-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 244-262
• Serial:
  • Journal of Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 3
  • Publisher: Taylor & Francis
  • ISSN: 1547-2450
  • EISSN: 1547-2442
  • Serial URL: http://www.tandfonline.com/loi/gits20

Subject/Index Terms

• TRT Terms: Automation; Autonomous intelligent cruise control; Car following; Highway capacity; Intelligent vehicles; Merging traffic
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01673721
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 26 2018 10:14AM