A Study of Association Feature Mining for Risky Driving Behavior Considering the Influence of Traffic Signs

Risky driving behavior is an important factor in inducing chaotic road conditions and crash occurrences. The purpose of this study is to examine the association between risky driving behaviors and various factors, including roadway alignment, facility conditions, traffic operating conditions, and weather. First, the study uses kernel density analysis to explore the locations of risky driving behaviors. Next, the K-means algorithm to categorize continuous variables. Finally, the classical Apriori algorithm was used to mine the associative relationships of risky driving behaviors in different situations. It was found that frequent risky driving behaviors were mainly distributed near interchanges, which were related to the influence of merging areas, complex signs, and sign types. In addition, weather and road alignment were also found to be associated with risky driving behavior. The results of this study are useful for observing the association of risky driving behaviors in different situations, and then proposing targeted safety control.

• Record URL:
  • https://doi.org/10.1061/9780784485484.195
• 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:
  • Liu, Chang
  • Zhao, Xiaohua
  • Ou, Jushang
  • Yao, Ying
  • Zhang, Wenjie
• 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 2067-2077
• Monograph Title: CICTP 2024: Resilient, Intelligent, Connected, and Low-Carbon Multimodal Transportation

Subject/Index Terms

• TRT Terms: Driving behavior; Interchanges; Risk assessment; Risk taking; Traffic signs; Weather conditions
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01940952
• Record Type: Publication
• ISBN: 9780784485484
• Files: TRIS, ASCE
• Created Date: Dec 26 2024 9:35AM