Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < " < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

• Record URL:
  https://doi.org/10.3141/2622-10
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309441889
• Authors:
  • Yang, Da
  • Qiu, Xiaoping
  • Ma, Lina
  • Wu, Danhong
  • Zhu, Liling
  • Liang, Hongbin
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 105–116
• Monograph Title: Traffic Flow Theory and Characteristics, Volume 1
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2622
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Cellular automata; Highway capacity; Intelligent vehicles; Lane changing; Reaction time; Traffic congestion; Traffic density; Vehicle mix
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01628733
• Record Type: Publication
• ISBN: 9780309441889
• Report/Paper Numbers: 17-04302
• Files: TRIS, TRB, ATRI
• Created Date: Mar 10 2017 9:21AM