Vehicle-Specific Virtual Traffic Control Strategy to Reduce the Start-Up Delay for Autonomous Heavy Trucks
Heavy trucks, with their slow acceleration and reduced speed, can cause traffic delays at intersections, thereby affecting the efficiency of traffic flow. To address this issue, this paper introduces a virtual traffic control system called advanced stop point and prior start time (ASP-PST). The ASP-PST leverages vehicle-to-infrastructure (V2I) communications between traffic signals and connected and automated heavy trucks. The system guides autonomous heavy trucks on where to stop and when to initiate their movement ahead of the green light. This strategy reduces start-up delays by managing vehicle movement control, enabling trucks to reach sufficient speed before approaching the intersection, thereby ensuring smooth passing and harmonized traffic flow. An analytical solution for the ASP-PST has been developed and validated through microscopic simulation tests at signalized intersections. The results show reductions in travel time of up to 50% in mixed traffic conditions, consisting of both heavy trucks and general cars, and illustrate the formation of well-coordinated platoons with uniform spacing. Demonstrating efficacy across various network environments, the ASP-PST offers a potential solution to enhance traffic flow and reduce congestion caused by heavy trucks at intersections.
Authors
- Kyongwon Kim (ORCID: https://orcid.org/0000-0002-8091-5686)
- Haesung Ahn (ORCID: https://orcid.org/0000-0003-0938-0989)
- Yohee Han (ORCID: https://orcid.org/0000-0002-7016-273X)
- Yong‐Hoon Kim (ORCID: https://orcid.org/0000-0001-5209-4908)
- Saeideh Esmaeili
Institutions
- University of Seoul (KR)
- Ewha Womans University (KR)
- British Columbia Institute of Technology (CA)
- Yonsei University (KR)
- University of Windsor (CA)
Publication Details
- Journal
- IEEE Transactions on Intelligent Vehicles
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1109/tiv.2024.3494873
- Citations
- 1
- Primary Topic
- Transportation and Mobility Innovations
- Type
- article
- Field-Weighted Citation Impact
- 0.20