TY  - JOUR
AU  - Shuqair, Amal 
AU  - Kozaitis, Samuel 
PY  - 2017
TI  - Block-Matching Twitter Data for Traffic Event Location
JF  - American Journal of Engineering and Applied Sciences
VL  - 10
IS  - 2
DO  - 10.3844/ajeassp.2017.348.352
UR  - https://thescipub.com/abstract/ajeassp.2017.348.352
AB  - 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.