Optimum Connected Vehicle Speed Control on Signalized Roadways in Mixed Flow

Previous studies have shown that the optimal speed trajectories for vehicles with different engine types (e.g., gasoline versus electric vehicles) are very different under certain conditions. This study aims to solve this issue by developing a general speed control algorithm that calculates a compromised solution across different vehicle engine types while optimizing the entire mixed traffic flow in the network. The proposed algorithm optimizes vehicle trajectories for mixed traffic flow, including both internal combustion engine vehicles (ICEVs) and battery electric vehicles (BEVs). To investigate the performance of the proposed controller under various traffic demand levels, a case study was designed using a simulated arterial corridor with three signalized intersections. The algorithm for mixed flow was compared with the algorithms previously developed for each individual vehicle type to investigate the system-level performances. Test results demonstrate that the proposed controller for mixed flow outperforms the previously developed controllers for individual vehicle models by further reducing fuel consumption, battery energy, and traffic delay under various traffic demand levels. Lastly, the proposed algorithm was used to develop a speed guidance system that provides two options of output: (1) recommended speed value, and (2) color-coded speed guidance. The developed speed guidance system was coded into a DLL file by the Delphi coding program and can be directly used in driving simulators to test human responses to two options of driving guidance and their corresponding performances. The speed guidance system was implemented in the driving simulator, and participants are given a color-coded speed recommendation through the entire route in different scenarios. Participants’ driving behaviors in various speed guidance scenarios are compared with those driving the same route without any speed guidance. Descriptive and statistical analyses including ANOVA, Post hoc Tukey and regressions are performed on the data obtained from 15 participants with various sociodemographic backgrounds. The study reveals that sociodemographic factors, such as gender and age, influence the effectiveness of the speed guidance system. Female drivers exhibit lower compliance with speed guidance, while older drivers face challenges in following the recommendations.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 49p

Subject/Index Terms

Filing Info

  • Accession Number: 01893349
  • Record Type: Publication
  • Report/Paper Numbers: UMEC-044
  • Contract Numbers: 69A43551747123
  • Created Date: Sep 18 2023 8:49AM