Social Data Mining for Understanding Public Perceptions of Autonomous Vehicles: National Trends and the Case of Florida

Automated vehicles (AV) represent one of the most exciting areas of transportation today as they have begun to capture the public interest, with the technology moving closer and closer to widespread real-world implementation. The authors are in the early stages of a long transitional phase, and knowledge of how the public perceives these new technologies is presently limited. Knowledge of how the public sees these new technologies can help inform transportation planning and policy efforts aimed at ensuring a smooth transition to automated vehicles. Capturing interest in public opinion and sentiment on transportation policy issues is nothing new, but what is possible now is extracting such knowledge from on-line social media. Data from on-line social media portals can be analyzed, or ‘mined,’ to learn how the public perceives transportation issues. This paper reports on research where a major task is to analyze social media data as a means of learning about the public’s perception of automated vehicles. Efforts primarily focus on experience with data geo-located collected from Twitter, a popular social media outlet. Results are presented for the national case with selected statistics broken out for the state of Florida. Overall, there is a great deal of spatial variability in where and the extent to which AV is being discussed, though discussion to date has tended to be positive in nature.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ40 Standing Committee on Travel Survey Methods.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Horner, Mark W
  • Richard, Amanda
• Conference:
  • Transportation Research Board 95th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2016-1-10 to 2016-1-14
• Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Maps; References; Tables;
• Pagination: 15p
• Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Data analysis; Data mining; Intelligent vehicles; Public opinion; Social media
• Identifier Terms: Twitter
• Geographic Terms: Florida
• Subject Areas: Data and Information Technology; Highways; Society; Vehicles and Equipment;

Filing Info

• Accession Number: 01592739
• Record Type: Publication
• Report/Paper Numbers: 16-3786
• Files: TRIS, TRB, ATRI
• Created Date: Mar 4 2016 5:04PM