What do autonomous vehicles mean to traffic congestion and crash? Network traffic flow modeling and simulation for autonomous vehicles

Transportation infrastructure is quickly moving towards revolutionary changes to accommodate the deployment of autonomous vehicles (AVs). On the other hand, the transition to new vehicle technologies will be shaped in large part by changes in performance of roadway infrastructure. This research aims at understanding the relationship between AV technology and infrastructure performance, which leads to revolutionary change in transportation infrastructure design in the both short and long term. To assess the vehicular technology impact to the traffic flow, two of the most important questions the project team attempts to tackle in this research are: 1) How would vehicle automation/communication, with different sensing and control specifications, change the vehicle speed and headway under various traffic conditions, and therefore change traffic congestion and crash patterns in the network? and 2) How would the vehicular technology change the flow capacity of the roadway infrastructure network, under different crash rates that are expected to be achieved by different vehicular control strategies? How does the change vary at different levels of AV penetration rates? This project primarily addresses the mobility concerns of AVs, while establishing a modeling framework that allows future extensions to assess both mobility and safety. In particular, this research proposes a multi-class traffic flow model that captures the car-following behavior of both regular vehicles and AVs.

• Record URL:
  http://www.utc.ices.cmu.edu/utc/tier-one-reports/Qian1_TSETFinalReport.pdf
• 
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Technologies for Safe and Efficient Transportation University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

  Research and Innovative Technology Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Qian, Sean
  • Yang, Shuguan
• Publication Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Research Report
• Pagination: 6p

Subject/Index Terms

• TRT Terms: Car following; Crash rates; Headways; Highway capacity; Infrastructure; Intelligent vehicles; Speed; Traffic congestion; Traffic flow; Traffic simulation
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01603547
• Record Type: Publication
• Contract Numbers: DTRT12GUTG11
• Files: UTC, TRIS, RITA, ATRI, USDOT
• Created Date: Jun 28 2016 4:41PM