What do riders say and where? The detection and analysis of eyewitness transit tweets

Information shared on social media by transit system customers is often candid, localized, and includes in the moment information about emerging events or issues. Twitter provides an unfiltered and timestamped feed of information that can be aggregated to generate valuable insights. The authors research aims to identify passenger-related transit incidents from a public Twitter feed. Detecting these incidents in real time enables transit agencies to immediately respond to them by dispatching security, safety, or maintenance crews or by rapidly replying to requests made to the agency that are urgent in nature. They leverage natural language processing to extract latent information from identified eyewitness tweets about transit, focusing on location details, topic classification, and sentiment analysis. They outline an approach to developing a useful corpus of transit-focused tweets, detecting in the moment events, classifying these tweets into topics, and detecting locations where possible. They then demonstrate the approach through an application to Calgary Transit in Calgary, Canada. The results demonstrate that key incidents can be identified and prioritized for an agency.

• Record URL:
  https://doi.org/10.1080/15472450.2022.2026773
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
• Supplemental Notes:
  • © 2022 Taylor & Francis Group, LLC. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Kabbani, O
  • Klumpenhouwer, W
  • El-Diraby, T
  • Shalaby, A
• Publication Date: 2023-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 347-363
• Serial:
  • Journal of Intelligent Transportation Systems
  • Volume: 27
  • Issue Number: 3
  • Publisher: Taylor & Francis
  • ISSN: 1547-2450
  • EISSN: 1547-2442
  • Serial URL: http://www.tandfonline.com/loi/gits20

Subject/Index Terms

• TRT Terms: Attitudes; Customer satisfaction; Incident management; Location data; Social media; Transit riders
• Identifier Terms: Twitter
• Subject Areas: Data and Information Technology; Passenger Transportation; Public Transportation; Safety and Human Factors;

Filing Info

• Accession Number: 01881065
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 25 2023 9:49AM