Analyzing Differences between Questionnaire-Surveyed Driving Style and Revealed Style from Driving Simulation Experiments

Variations in driving styles among individuals lead to distinctive decision-making and behavioral characteristics. Investigating these characteristics of interaction behavior is vital for accurately predicting the behavior of autonomous vehicles. Many researchers employ questionnaires to determine driving styles, yet few validate whether questionnaire outcomes align with data analysis results. In this study, the authors establish a driving simulation experiment platform to collect driver behavior data in a controlled setting, particularly focusing on unprotected left-turn intersections. The authors design a questionnaire to assess drivers’ subjective perceptions of their driving styles, aligning group interaction experiments accordingly. The authors compile vehicle trajectory data (encompassing transverse and longitudinal velocity, acceleration, etc.) and driver operation data (including pedal force and steering wheel angle) from 134 experiments involving 10 drivers. By comparing self-reported driving styles with model outcomes and questionnaire results, the authors’ analysis reveals substantial differences in the composition and proportions of driving styles, especially between straight-line driving and left turns. It becomes evident that even drivers identifying as conservative can exhibit occasional aggressiveness.

• Record URL:
  • https://doi.org/10.1061/9780784485484.205
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784485484
• Supplemental Notes:
  • © 2024 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhang, Yulin
  • Ni, Ying
• Conference:
  • 24th COTA International Conference of Transportation Professionals
  • Location: Shenzhen , China
  • Date: 2024-7-23 to 2024-7-26
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 2174-2183
• Monograph Title: CICTP 2024: Resilient, Intelligent, Connected, and Low-Carbon Multimodal Transportation

Subject/Index Terms

• TRT Terms: Driving behavior; Driving simulators; Left turns; Questionnaires; Revealed preferences; Stated preferences
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01941319
• Record Type: Publication
• ISBN: 9780784485484
• Files: TRIS, ASCE
• Created Date: Dec 30 2024 9:58AM