Data mining Twitter to Improve Automated Vehicle Safety

Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, the authors developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. The authors used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. The authors combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.

• Record URL:
  https://safed.vtti.vt.edu/wp-content/uploads/2021/02/Final-Safe-D-Final-Research-Report-04-098-Final-Corrected.pdf
• Summary URL:
  https://safed.vtti.vt.edu/projects/data-mining-twitter-to-improve-automated-vehicle-safety/
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/56364
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Safety Through Disruption University Transportation Center (Safe-D)

  Texas A&M Transportation Institute
  College Station, TX  United States 

  Virginia Polytechnic Institute and State University, Blacksburg

  Virginia Tech Transportation Institute
  3500 Transportation Research Plaza
  Blacksburg, VA  United States  24061

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • McDonald, Anthony D
  • 0000-0001-7827-8828
  • Huang, Bert
  • 0000-0002-8548-7246
  • Wei, Ran
  • 0000-0001-7982-0404
  • Alambeigi, Hananeh
  • 0000-0003-4310-3950
  • Arachie, Chidubem
  • 0000-0002-1953-6206
  • Smith, Alec
  • 0000-0002-1938-4660
  • Jefferson, Jaycelyn
  • 0000-0001-7461-6762
• Publication Date: 2021-2

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; References; Tables;
• Pagination: 40p

Subject/Index Terms

• TRT Terms: Attitudes; Autonomous vehicles; Communications; Data mining; Guidelines; Machine learning; Social media; Traffic crashes; Vehicle safety
• Identifier Terms: Twitter
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01776518
• Record Type: Publication
• Report/Paper Numbers: 04-098
• Contract Numbers: 69A3551747115
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Jul 14 2021 1:43PM