# US 10,373,488 B2 · Traffic monitoring systems and methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,373,488 B2 - Document ID: US10373488B2 - Status: Granted - Kind: B2 - Application number: 15/597,047 - Filed: 2017-05-16 - Published: 2017-08-31 - Granted: 2019-08-06 - Earliest claimed: 2008-04-23 - Assignees: Verizon Patent and Licensing Inc. - Applicants: Verizon Patent and Licensing Inc. - Inventors: Donald H. Relyea; Michelle Felt - Web page: https://patentgrep.com/patent/US10373488B2 ## CPC - G08G 1/012: {from other sources than vehicle or roadside beacons, e.g. mobile networks} - G08G 1/00: Traffic control systems for road vehicles (arrangement of road signs or traffic signals E01F9/00 {; automatic vehicle control B62D}) - G08G 1/0104: {Measuring and analyzing of parameters relative to traffic conditions} - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/0141: {for traffic information dissemination} - G08G 1/052: with provision for determining speed or overspeed {(speed measuring in general G01P)} - G08G 1/056: with provision for distinguishing direction of travel - G08G 1/094: {Hardware aspects; Signal processing or signal properties, e.g. frequency bands} - G08G 1/09626: {where the origin of the information is within the own vehicle, e.g. a local storage device, digital map} - G08G 1/096716: {where the received information does not generate an automatic action on the vehicle control} - G08G 1/09675: {where a selection from the received information takes place in the vehicle} - G08G 1/096775: {where the origin of the information is a central station} - H04L 41/22: comprising specially adapted graphical user interfaces [GUI] - H04W 4/027: {using movement velocity, acceleration information} - H04W 4/50: Service provisioning or reconfiguring - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract In an exemplary method, a traffic monitoring subsystem receives mobile device attribute data from a plurality of mobile devices over a network, selectively aggregates the mobile device attribute data, the selectively aggregating including identifying anomalous data in the mobile device attribute data and excluding the anomalous data from the aggregated mobile device attribute data, and generating traffic condition data based at least in part on the aggregated mobile device attribute data, the traffic condition data representative of a traffic condition. In certain examples, the traffic monitoring system provides the traffic condition data to at least one of the mobile devices. ## Claims ### Claim 1 A method comprising: identifying, by a traffic monitoring subsystem, a set of geographic location coordinates that define a traffic monitoring location; receiving, by the traffic monitoring subsystem, Global Positioning System (“GPS”) location data from a plurality of mobile devices over a network, the GPS location data generated by GPS sensors within the plurality of mobile devices and representative of a plurality of geographic locations associated with mobile devices within the traffic monitoring location and mobile devices outside of the traffic monitoring location; selectively aggregating, by the traffic monitoring subsystem, the GPS location data, the selectively aggregating comprising: filtering out, from the received GPS location data, irrelevant GPS location data representative of the geographic locations associated with the mobile devices outside of the traffic monitoring location; identifying, within the GPS location data representative of the geographic locations associated with the mobile devices within the traffic monitoring location, anomalous data by determining that the anomalous data does not accurately represent traffic flow at the traffic monitoring location, and excluding the anomalous data from the aggregated GPS location data; generating, by the traffic monitoring subsystem, traffic condition data based at least in part on the aggregated GPS location data, the traffic condition data representative of a traffic condition; and providing, by the traffic monitoring subsystem, the traffic condition data to at least one of the mobile devices in the plurality of mobile devices. ### Claim 2 (depends on 1) The method of claim 1, wherein the identifying of the anomalous data comprises identifying the anomalous data based at least on speed data associated with the plurality of mobile devices. ### Claim 3 (depends on 1) The method of claim 1, wherein the identifying of the anomalous data comprises identifying the anomalous data based at least on directional heading data associated with the plurality of mobile devices. ### Claim 4 (depends on 1) The method of claim 1, wherein the identifying of the anomalous data comprises identifying the anomalous data based at least on signal strength data associated with the plurality of mobile devices. ### Claim 5 (depends on 1) The method of claim 1, wherein the identifying of the anomalous data comprises identifying the anomalous data based on the anomalous data having at least a threshold level of variation from other data included in the GPS location data. ### Claim 6 (depends on 1) The method of claim 1, wherein the providing of the traffic condition data to the at least one of the mobile devices is based on the geographic locations of the mobile devices in relation to the traffic monitoring location. ### Claim 7 (depends on 1) The method of claim 1, further comprising: determining, by the traffic monitoring subsystem, a confidence factor associated with said traffic condition data; and providing, by the traffic monitoring subsystem, the confidence factor, together with the traffic condition data, to at least one of the mobile devices. ### Claim 8 (depends on 7) The method of claim 7, wherein the confidence factor is determined based at least in part on a number of the mobile devices corroborating the traffic condition data. ### Claim 9 A method comprising: identifying, by a traffic monitoring subsystem, a set of geographic location coordinates that define a traffic monitoring location; selectively aggregating, by the traffic monitoring subsystem, Global Positioning System (“GPS”) location data received from a plurality of mobile devices over a network, the GPS location data generated by GPS sensors within the plurality of mobile devices and representative of a plurality of geographic locations associated with mobile devices within the traffic monitoring location and mobile devices outside of the traffic monitoring location, the selectively aggregating comprising: filtering out, from the received GPS location data, irrelevant GPS location data representative of the geographic locations associated with the mobile devices outside of the traffic monitoring location; identifying, within the GPS location data representative of the geographic locations associated with the mobile devices within the traffic monitoring location, anomalous data by determining that the anomalous data does not accurately represent traffic flow at the traffic monitoring location, and excluding the anomalous data from the aggregated GPS location data; generating, by the traffic monitoring subsystem, traffic condition data based at least in part on the aggregated GPS location data, the traffic condition data representative of a traffic condition; and providing, by the traffic monitoring subsystem, the traffic condition data to at least one of the mobile devices in the plurality of mobile devices based on the geographic locations of the mobile devices in relation to the traffic monitoring location. ### Claim 10 (depends on 9) The method of claim 9, wherein the identifying of the anomalous data comprises identifying the anomalous data based on at least one of speed data and directional heading data associated with the plurality of mobile devices. ### Claim 11 A system comprising: at least one physical computing device that: identifies a set of geographic location coordinates that define a traffic monitoring location; receives Global Positioning System (“GPS”) location data from a plurality of mobile devices over a network, the GPS location data generated by GPS sensors within the plurality of mobile devices and representative of a plurality of geographic locations associated with mobile devices within the traffic monitoring location and mobile devices outside of the traffic monitoring location; selectively aggregates the GPS location data received from the plurality of mobile devices over the network, the selective aggregation comprising: filtering out, from the received GPS location data, irrelevant GPS location data representative of the geographic locations associated with the mobile devices outside of the traffic monitoring location; identifying, within the GPS location data representative of the geographic locations associated with the mobile devices within the traffic monitoring location, anomalous data by determining that the anomalous data does not accurately represent traffic flow at the traffic monitoring location, and excluding the anomalous data from aggregated GPS location data; and generates traffic condition data based at least in part on the aggregated GPS location data, the traffic condition data representative of a traffic condition; and provides the traffic condition data to at least one of the mobile devices in the plurality of mobile devices. ### Claim 12 (depends on 11) The system of claim 11, wherein the identifying of the anomalous data comprises identifying the anomalous data based at least on speed data associated with the plurality of mobile devices. ### Claim 13 (depends on 11) The system of claim 11, wherein the identifying of the anomalous data comprises identifying the anomalous data based at least on directional heading data associated with the plurality of mobile devices. ### Claim 14 (depends on 11) The system of claim 11, wherein the identifying of the anomalous data comprises identifying the anomalous data based at least on signal strength data associated with the plurality of mobile devices. ### Claim 15 (depends on 11) The system of claim 11, wherein the identifying of the anomalous data comprises identifying the anomalous data based on the anomalous data having at least a threshold level of variation from other data included in the GPS location data. ### Claim 16 (depends on 11) The system of claim 11, wherein the at least one physical computing device provides the traffic condition data to the at least one of the mobile devices based on the geographic locations of the mobile devices in relation to the traffic monitoring location. ### Claim 17 (depends on 11) The system of claim 11, wherein the at least one physical computing device further: determines a confidence factor associated with said traffic condition data; and provides the confidence factor, together with the traffic condition data, to at least one of the mobile devices. ### Claim 18 (depends on 17) The system of claim 17, wherein the confidence factor is determined based at least in part on a number of the mobile devices corroborating the traffic condition data. ### Claim 19 (depends on 9) The method of claim 9, further comprising: determining, by the traffic monitoring subsystem, a confidence factor associated with said traffic condition data; and providing, by the traffic monitoring subsystem, the confidence factor, together with the traffic condition data, to at least one of the mobile devices. ### Claim 20 (depends on 19) The method of claim 19, wherein the confidence factor is determined based at least in part on a number of the mobile devices corroborating the traffic condition data.