Speed Harmonization Using Optimal Control Algorithm Under Mixed Traffic of Connected-Automated and Human Driven Vehicles

The operations of the state-of-the-art speed harmonization strategies are mostly based on heuristic solution approaches that do not guarantee system optimality. In addition, they are not likely suitable for field implementation due to their computational burden entailed to the key strategy in the proactive Variable Speed Limit (VSL) algorithm, so called Model Predictive Control (MPC) method. By leveraging the automated vehicle and radar sensor technologies, a novel speed harmonization algorithm was developed using an optimal control theory. The Hamiltonian method (i.e. Pontryagin’s principle) was used to solve the speed harmonization optimization problem. Unlike the MPC method which requires model development and calibration process, the optimal control theory analytically determines the solution while constrained by vehicle dynamics. The objective of the algorithm is to minimize the acceleration variations under hard safety constraint to avoid rear-end collision.The impacts of the optimal control algorithm were examined for the case of speed reduction zone on a freeway under various market penetrations of automated vehicles. Its performance was then compared with that of the base case of human drivers and the two types of state-of-the-art speed harmonization algorithms, i.e., section-based and the vehicle-based speed harmonization algorithms. This study used an advanced simulation test-bed integrating the optimal control algorithm and a microscopic traffic simulator under the C# programming environment. A polynomial meta-model was used to estimate fuel consumption.The optimal control algorithm showed significant improvements in mobility, fuel consumption, and safety compared to those of the base case and the state-of-the-art speed harmonization strategies under varying market penetrations of automated vehicles. Clearly, the optimal control algorithm performed best under 100% market penetration of automated vehicles. The simulation results showed that the travel time was improved by 4-28% and the fuel consumptions was improved by 6-21% for different market penetrations of AVs. This study demonstrated the feasibility of the control algorithm under mixed traffic and provided quantitative assessment in various aspects of mobility, fuel economy and safety compared to the state of the art SPD-HAR algorithms.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AHB30 Standing Committee on Vehicle-Highway Automation. Speed Harmonization Using Optimal Control Algorithm Under Mixed Traffic of Automated and Human-Driven Vehicles: This is an alternate title.
• Authors:
  • Hong, Seongah
  • Malikopoulos, Andreas A
  • Park, Byungkyu Brian
  • Lee, Joyoung
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 5p

Subject/Index Terms

• TRT Terms: Algorithms; Connected vehicles; Speed control; Traffic control; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01660969
• Record Type: Publication
• Report/Paper Numbers: 18-06228
• Files: TRIS, TRB, ATRI
• Created Date: Feb 22 2018 9:19AM