Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models

Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, the authors present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. The authors examine the principle of optimality conditions and resulting computational complexity under different coupling conditions.

• Record URL:
  https://doi.org/10.1016/j.trb.2017.10.012
• Record URL:
  https://www.sciencedirect.com/science/article/pii/S0191261517301078
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Wei, Yuguang
  • Avci, Cafer
  • Liu, Jiangtao
  • Belezamo, Baloka
  • Aydin, Nizamettin
  • Li, Pengfei(Taylor)
  • Zhou, Xuesong
• Publication Date: 2017-12

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 102-129
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 106
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Car following; Connected vehicles; Dynamic programming; Integer programming; Intelligent vehicles; Optimization; Traffic flow; Trajectory
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01655653
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 2 2018 10:38AM