Drivers’ Reaction Analysis and Prediction with V2I-Communication-Based Energy-Related Strategies

Connected vehicle technology provides promising opportunities to improve road safety, enhance traffic efficiency, lower fuel consumption, and reduce emissions. It has been suggested that if drivers comply with recommendations provided by wireless communications, connected vehicle technology can bring huge benefits. However, whether drivers will accept these suggestions and what variables will influence their behavior under a connected driving environment have not been systematically studied in terms of safety and energy consumption. In addition, no modeling efforts has been conducted to predict drivers’ reactions based on corresponding variables. This paper aims to assess and model drivers’ acceptance and behavior when receiving energy-related speed recommendations through Vehicle-to-Infrastructure communications. A mixed-subject-design experiment was conducted in a closed-loop test track, Mcity, with seven intersection maneuver scenarios. A generally high compliance rate to the recommended energy-related strategy was observed: 72% of the time, drivers changed their intersection-approaching behavior to follow the recommendations. Mixed models were established to explore the impacting factors while Principal Component Analysis was used to classify subjective data into four groups. To predict drivers’ reactions when offered a speed suggestion, Random Forests were built with 13 independent variables, derived from four categories: vehicle kinematic features, device information, driver characteristics, and subjective data. Using this model, drivers’ reactions at each intersection could be predicted by the data obtained about 87.4m away from the intersection, where the vehicle started to receive signal phasing and timing information. Findings in this study can contribute to the optimization of energy-saving algorithms and the improvement of driving safety.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AND20 Standing Committee on User Information Systems.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Yu, Bo
    • Bao, Shan
    • Feng, Fred
    • Sayer, James
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01698216
  • Record Type: Publication
  • Report/Paper Numbers: 19-04240
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:49AM