Microscopic Modeling of the Effects of Autonomous Vehicles on Motorway Performance

Autonomous vehicles (AVs) have been the subject of extensive research in recent years and are expected to completely transform the operation of transport networks and revolutionize the automotive industry in the coming decades. Modeling detailed interactions among vehicles with varying levels of penetration rates is essential for evaluating the potential effects. One such investigation is being performed within the ‘HumanDrive’ Project in the U.K. This work has required the development of a behavioral model that incorporates microscopic level interactions and has been based on a pre-existing adaptive cruise control and lane-changing model that has been adapted to better replicate the limitations of AVs and allow the investigation of differing levels of intelligence or assertiveness. The model has been implemented on the M1 Motorway near Sheffield in the U.K. This has allowed the investigation of the effects of AVs on the operation of a real network under various traffic conditions where the overall effects may be revealed, both as advantages to AV drivers, and potentially disadvantages to non-AV traffic. Additionally, it has been possible to examine how these affect junction operations and net emissions. Preliminary results have allowed us to quantify the positive effects of AVs which increase with the penetration. However, it is clear that there are points of inflection where benefits start to slow. It is at these (high) penetration rates that initial operational assumptions may become increasingly stretched and additional infrastructure and cooperative systems are likely to have to become prevalent.

• Record URL:
  https://doi.org/10.1177/0361198120949243
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • © National Academy of Sciences: Transportation Research Board 2020.
• Authors:
  • Mesionis, George
  • Brackstone, Mark
  • Gravett, Natalie
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 697-707
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2674
  • Issue Number: 11
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Autonomous intelligent cruise control; Autonomous vehicles; Freeway operations; Freeways; Lane changing; Microscopic traffic flow; Vehicle mix
• Identifier Terms: M1 Motorway
• Geographic Terms: United Kingdom
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01752343
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Sep 17 2020 5:54PM