What do Riders Say and Where? The Detection and Analysis of Eyewitness Transit Tweets

Analyzing Twitter data is useful for planning and operating transportation systems. Twitter provides an unfiltered and timestamped feed of information that can be aggregated to generate valuable insights. The authors' research creates a framework for processing a public Twitter feed to identify passenger–related transit incidents. Detecting these incidents in real time enables transit agencies to immediately respond to them by dispatching security, safety, or maintenance crews, and in the context of the current COVID–19 pandemic, to provide targeted cleaning measures to combat the spread of the virus. Using natural language processing, the authors identify eyewitness tweets about transit and then extract latent information from the tweets such as location details, sentiments, and topics. This enables agencies to respond to an incident faster and to identify spatial and temporal patterns for incidents and interests throughout the network.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 25p

Subject/Index Terms

Filing Info

  • Accession Number: 01764444
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-01666
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:05AM