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Disruption or Adoption? Understanding Perceptions of Driverless Vehicles by Fusing Social Media Data Mining and Survey Data

Presenters Name: 
Max Zheng
Co Presenters Name: 
Primary Research Mentor: 
Andrew Mondschein
Secondary Research Mentor: 
9:30 - 10:15
Time of Presentation: 
2019 - 9:30am to 10:15am
Newcomb Hall Ballroom
Presentation Type: 
Presentations Academic Category: 
Social Science
Grant Program Recipient: 
Double Hoo Research Grant

Increased travel as a result of urbanization and population growth has led to the need for safer, more efficient transportation in US cities. This research examines whether the public believes driverless transportation systems could meet this demand by combining public social media data from Twitter and survey data from Crozet - a pilot site for driverless shuttle adoption. Using mined Twitter data (2012 - present), a two-pronged approach was conducted to understand public perceptions of driverless technology. Natural Language Processing using topic modeling was chosen to infer latent topics of interest related to driverless technology and a sentiment analysis model was developed to uncover the public dominant emotions towards each area of interest. The social media analysis uncovered a set of 5 hidden themes consisting of Safety, Technology Development, Industrial / System Integration, Milestone and Vision, and Service Types as well as their classified opinions of positive and negative. Survey data results indicate greater concern for current implications of driverless technology adoption rather than future vision. Both social media and survey data indicate that safety, technological progress, and industrial and urban integration are of major concern, while financial implications of adoption appear mainly in local survey data. Driverless vehicle developers can leverage these results to influence what functionalities they should improve upon, and how they can shape their marketing campaigns to cater to customers’ needs and expectations. More importantly, the findings will help public transport operators and city planners as they attempt to integrate autonomous vehicles into the urban transportation system.