Recasting and optimizing intersection automation as a connected-and-automated-vehicle (CAV) scheduling problem: A sequential branch-and-bound search approach in phase-time-traffic hypernetwork

It is a common vision that connected and automated vehicles (CAVs) will increasingly appear on the road in the near future and share roads with traditional vehicles. Through sharing real-time locations and receiving guidance from infrastructure, a CAV's arrival and request for green light at intersections can be approximately predicted along their routes. When many CAVs from multiple approaches at intersections place such requests, a central challenge is how to develop an intersection automation policy (IAP) to capture complex traffic dynamics and schedule resources (green lights) to serve both CAV requests (interpreted as request for green lights on a particular signal phase at time t) and traditional vehicles. To represent heterogeneous vehicle movements and dynamic signal timing plans, the authors first formulate the IAP optimization as a special case of machine scheduling problem using a mixed integer linear programming formulation. Then the authors develop a novel phase-time-traffic (PTR) hypernetwork model to represent heterogeneous traffic propagation under traffic signal operations. Since the IAP optimization, by nature, is a special sequential decision process, the authors also develop sequential branch-and-bound search algorithms over time to IAP optimization considering both CAVs and traditional vehicles in the PTR hypernetwork. As the critical part of the branch-and-bound search, special dominance and bounding rules are also developed to reduce the search space and find the exact optimum efficiently. Multiple numerical experiments are conducted to examine the performance of the proposed IAP optimization approach.

• Record URL:
  https://doi.org/10.1016/j.trb.2017.09.020
• Record URL:
  https://www.sciencedirect.com/science/article/pii/S0191261517304782
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Li, Pengfei (Taylor)
  • Zhou, Xuesong
• Publication Date: 2017-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 479-506
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 105
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Branch and bound algorithms; Connected vehicles; Intelligent vehicles; Optimization; Signalized intersections; Traffic signal control systems; Traffic signal phases
• Uncontrolled Terms: Hypernetworks
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01651278
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 20 2017 5:09PM