Optimisation of lane-changing advisory at the motorway lane drop bottleneck

Reduction in the number of lanes (lane drop) is common on motorways due to road design, incidents, or road maintenance, and it can be an active bottleneck if the traffic demand is high. If congestion occurs, the lane drop capacity will decrease 10–20%. In order to avoid capacity drop, this study analysed the reason and proposed lane-changing advisory control on the merge lane to distribute lane-changing using Cooperative Intelligent Transport Systems (C-ITS) technology. Further, this study used hyper-heuristic optimisation to obtain the lane-changing advisory proportion of each segment upstream of lane drop. Conditions of different traffic demands were analysed using the microscopic traffic simulation software AIMSUN and its Application Program Interface (API) function. Results indicated that the proposed lane-changing advisory strategy could reduce traffic congestion and obviously improve traffic efficiency. This study also analysed the effects with different proportions of connected vehicles and found that if the connected vehicle ratio is less than 20%, the lane-changing advisory has little impact on the lane drop performance, and that if the penetration rate is more than 50%, the gain in performance is marginal.

• Record URL:
  https://doi.org/10.1016/j.trc.2019.07.016
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X19310010
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chung, Edward
  • 0000-0001-6969-7764
  • Sabar, Nasser R
  • Chung, Edward
  • 0000-0001-9679-5706
  • Bhaskar, Ashish
  • Guo, Xiucheng
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 303-316
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 106
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Bottlenecks; Connected vehicles; Intelligent transportation systems; Lane changing; Lane drops; Microscopic traffic flow; Optimization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01717511
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2019 3:07PM