American Journal of Engineering and Applied Sciences

Block-Matching Twitter Data for Traffic Event Location

Amal Shuqair and Samuel Kozaitis

DOI : 10.3844/ajeassp.2017.348.352

American Journal of Engineering and Applied Sciences

Volume 10, Issue 2

Pages 348-352

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.

Copyright

© 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.