Quantitatively mining and distinguishing situational discomfort grading patterns of drivers from car-following data

Situational discomfort awareness plays an important role in decision making among drivers and has rarely been discussed in detail in previous research. An instrumented vehicle was used to collect car-following data from multiple drivers, thereby quantitatively examining situational discomfort grading patterns using a new discomfort grading method and the latent Dirichlet allocation model. In this process, the gas pedal data and speed difference data are particularly involved in the computation for providing broader meaning to discomfort and building more comprehensive situations. The results show that individual discomfort awareness varies between drivers. More importantly, the potential patterns of situational discomfort grading are extracted, which provides knowledge for characterizing drivers in the context of discomfort awareness. The knowledge achieved can be further applied to distinguish drivers and identify the typical comfort and discomfort zones. This study has great value for promoting investigations on traffic psychology and developing more effective and customized driver assistant systems.

• Record URL:
  • https://doi.org/10.1016/j.aap.2018.12.006
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457518311126
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Qi, Geqi
  • 0000-0002-0767-1865
  • Guan, Wei
• Publication Date: 2019-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 282-290
• Serial:
  • Accident Analysis & Prevention
  • Volume: 123
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Alertness; Behavior; Car following; Data mining; Decision making; Drivers
• Uncontrolled Terms: Discomfort
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01691217
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 23 2019 5:09PM