Block-Matching Twitter Data for Traffic Event Location
Amal Shuqair1 & Samuel Kozaitis1 Show affiliations
- Florida Institute of Technology, United States
American Journal of Engineering and Applied Sciences, Volume 10, Issue 2, pp. 348–352 · doi.org/10.3844/ajeassp.2017.348.352
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.
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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
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