Analysis and comparison of auditory-cognitive and visual-manual distraction risk characteristics and their effect on driving

Distracted driving is one of the most important causes of traffic crashes, leading 162 countries to establish regulations to control distracted driving, mainly by prohibiting or limiting the use of mobile phones. At present, there is no standard for regulating cellphones or other distractions. Understanding the specific risk characteristics of common distracted driving behaviors is crucial for regulatory development. However, research has been limited in comparing the risk characteristics of common distractions. Hence, this study aims to analyze and compare the risk characteristics of several auditory-cognitive and visual-manual distractions, as well as factors specific to individual drivers. Two crash surrogate indicators, lateral position and acceleration, were categorized into three risk levels. The partial proportional odds model was used to analyze lane keeping events, and the mixed-effects logit model was used for speed control events. Model results showed that, compared with the no-distraction baseline, visual-manual distraction tasks undermined both driver lane control and speed control, while auditory-cognitive distraction had little effect on lane keeping; auditory-cognitive distractions showed less risk of max deceleration than visual-manual distractions, but showed more risk of max acceleration. Additionally, it was found that older and female drivers have a higher risk of max acceleration when distracted, and older drivers have a higher risk of lane departure when distracted. These results provide data support for the development of distracted driving regulations.

• Record URL:
  • https://doi.org/10.1016/j.trf.2024.11.002
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1369847824003048
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wu, Mengjiao
  • Wang, Xuesong
  • Chen, Jiawen
  • Ji, Xiang
  • Sun, Yiran
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1042-1061
• Serial:
  • Transportation Research Part F: Traffic Psychology and Behaviour
  • Volume: 107
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1369-8478
  • Serial URL: http://www.sciencedirect.com/science/journal/13698478

Subject/Index Terms

• TRT Terms: Distracted driving; Distraction; Driver performance; Lane keeping; Risk assessment; Surrogate safety measures
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01937789
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 22 2024 2:27PM