Block-Matching Twitter Data for Traffic Event Location
Journal Title: American Journal of Engineering and Applied Sciences - Year 2017, Vol 10, Issue 2
Abstract
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.
Authors and Affiliations
Amal Shuqair, Samuel Kozaitis
Keywords
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- EP ID EP194554
- DOI 10.3844/ajeassp.2017.348.352
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