@article {10.3844/ajeassp.2017.348.352, article_type = {journal}, title = {Block-Matching Twitter Data for Traffic Event Location}, author = {Shuqair, Amal and Kozaitis, Samuel}, volume = {10}, number = {2}, year = {2017}, month = {Apr}, pages = {348-352}, doi = {10.3844/ajeassp.2017.348.352}, url = {https://thescipub.com/abstract/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.}, journal = {American Journal of Engineering and Applied Sciences}, publisher = {Science Publications} }