Traffic monitoring systems and methods

• US 10,373,488 B2
• Filed: 05/16/2017
• Issued: 08/06/2019
• Est. Priority Date: 04/23/2008
• Status: Active Grant

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: Relyea, Donald H., Felt, Michelle
• Primary Examiner(s): Amin, Bhavesh V
• Application Number:
  US15/597,047
• Publication Number:
  US 20170249835A1
• Time in Patent Office: 812 Days
• Field of Search: None
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: G08G 1/056 with provision for distinguishing direction of travel
• CPC Class Codes: G08G 1/094 Hardware aspects; Signal processing or signal properties, e.g. frequency bands
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/09675 where a selection from the received information takes place in the vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/50 Service provisioning or reconfiguring

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

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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

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.

10. 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.

19. 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.

20. 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.

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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.