The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 24, 2015

Filed:

Oct. 20, 2011
Applicants:

Stephen Thomas Hardin, Suwanee, GA (US);

Thomas Rodger Becker, Atlanta, GA (US);

Charles A. Carey, Kirkland, WA (US);

Zachary Orlynn Morsbach, Cedar Park, TX (US);

Brad Webb, Katy, TX (US);

Inventors:

Stephen Thomas Hardin, Suwanee, GA (US);

Thomas Rodger Becker, Atlanta, GA (US);

Charles A. Carey, Kirkland, WA (US);

Zachary Orlynn Morsbach, Cedar Park, TX (US);

Brad Webb, Katy, TX (US);

Assignee:

AT&T Mobolity II LLC, Atlanta, GA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G08G 1/01 (2006.01); H04W 12/08 (2009.01);
U.S. Cl.
CPC ...
G08G 1/0112 (2013.01);
Abstract

The disclosed subject matter provides for traffic analysis employing timed fingerprint location information. In an aspect, TFL information can be associated with location characteristics for a UE. These location characteristics can describe the motion of the UE. As such, with proper analysis, the motion of the UE can be correlated to traffic patterns. Transportation analytics can employ TFL information to provide real time or near real time traffic information, forecast traffic conditions, or automate response to traffic conditions based on TFL information. Whereas TFL can provide advantages over other types of location information systems, leveraging TFL information in traffic analysis can reflect these advantages. Further, whereas TFL information can be gathered from nearly all modern and many legacy mobile devices, large volumes of TFL information can be employed in related transportation analytics.


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