Examining the potential of textual big data for public policy decision-making on driverless cars: A case study from Denmark

The simultaneous growth of textual data and the advancements within Text Analytics enables organisations to exploit this kind of unstructured data, and tap into previously hidden knowledge. However, the utilisation of this valuable resource is still insufficiently unveiled in terms of transport policy decision-making. This research aims to further examine the potential of textual data in transportation through a real-life case study. The case study, framed together with the Danish Road Directorate or Vejdirektoratet, was designed to assess public opinion towards the adoption of driverless cars in Denmark. Traditionally, the opinion of the public has often been captured by means of surveys for the problem owner. The authors' study provides demonstrations in which opinion towards the adoption of driverless cars is examined through the analysis of newspaper articles and tweets using topic modelling, document classification, and sentiment analysis. In this way, the research attends to the collective as well as individualised characteristics of public opinion. The analyses establish that Text Analytics may be used as a complement to surveys, in order to extract additional knowledge which may not be captured through the use of surveys. In this regard, the Danish Road Directorate could find the usefulness while understanding the barriers in the results generated from the authors' study, for supplementing their future data collection strategies.

• Record URL:
  https://doi.org/10.1016/j.tranpol.2020.05.026
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0967070X20303590
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/29485010
• Supplemental Notes:
  • © 2020 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Kinra, Aseem
  • Beheshti-Kashi, Samaneh
  • Buch, Rasmus
  • Sick Nielsen, Thomas Alexander
  • Pereira, Francisco
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 68-78
• Serial:
  • Transport Policy
  • Volume: 98
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0967-070X
  • Serial URL: http://www.elsevier.com/locate/issn/096707X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Case studies; Data analysis; Decision making; Machine learning; Transportation policy
• Geographic Terms: Denmark
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Policy; Vehicles and Equipment;

Filing Info

• Accession Number: 01744362
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 29 2020 11:21AM