AJEAS Cover
Frequency: Quarterly
ISSN: 1941-7020 (Print)
ISSN: 1941-7039 (Online)
Research Article Open Access

Block-Matching Twitter Data for Traffic Event Location

Amal Shuqair1 and Samuel Kozaitis1
  • 1 Florida Institute of Technology, United States

Abstract

We used a block-matching approach that is data-driven and relies mostly on patterns of tagged speech in Twitter streams as a way to identify events in road traffic. Events are useful because their location may identify the status of road segments, especially when cross-street data are available. Basing a system on patterns that are not pre-defined has the advantage of flexibility for a variety of scenarios.

American Journal of Engineering and Applied Sciences
Volume 10 No. 2, 2017, 348-352

DOI: https://doi.org/10.3844/ajeassp.2017.348.352

Submitted On: 9 March 2017 Published On: 12 April 2017

How to Cite: Shuqair, A. & Kozaitis, S. (2017). Block-Matching Twitter Data for Traffic Event Location. American Journal of Engineering and Applied Sciences, 10(2), 348-352. https://doi.org/10.3844/ajeassp.2017.348.352

Open AccessCopyright: © 2017 Amal Shuqair and Samuel Kozaitis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 3,015 Views
  • 1,717 Downloads
  • 0 Citations

Download

Keywords

  • Block-Matching
  • Parts of Speech
  • Social Media
  • Twitter