Adaptive traffic optimization using unmanned aerial vehicles

1. A method of adaptive traffic control, the method comprising:

receiving traffic data at an intersection;

determining statistics associated with traffic at the intersection based on the traffic data;

detecting an anomaly in the traffic based on the statistics;

utilizing an unmanned aerial vehicle (UAV) to determine a root cause of the anomaly; and

communicating an anomaly rule based on the root cause to a light controller to control traffic at the intersection.

2. The method of claim 1 , wherein determining the statistics comprises:

recording, for each of a plurality of time intervals and for each zone at the intersection, at least one of:

an average traffic flow rate for a corresponding zone; and

whether at least one object is detected in the corresponding zone.

3. The method of claim 2 , wherein the UAV is utilized if the average traffic flow rate is less than a threshold and no object is detected in the corresponding zone.

4. The method of claim 2 , wherein the anomaly is detected when the average traffic flow rate in a corresponding zone at the intersection is equal to or less than a threshold and no object is detected in the corresponding zone for at least two consecutive ones of the plurality of time intervals.

5. The method of claim 1 , wherein utilizing the UAV comprises:

retrieving settings for the UAV;

launching the UAV to monitor one or more target zones to determine the root cause; and

determining if, in one or more of the target zones, a corresponding traffic flow rate is less than a threshold and at least one object is detected therein.

6. The method of claim 5 , further comprising:

processing image data of the one or more target zones for which the corresponding traffic flow rate is less than the threshold;

sending the image data to a graphical user interface; and

determining the root cause of the anomaly based on at least one of a result of processing the image data or an input received from the graphical user interface in response to sending of the image data.

7. The method of claim 1 , further comprising:

determining the anomaly rule by:

retrieving an existing anomaly rule from an anomaly rules database; or

generating a new anomaly rule, wherein

the existing anomaly rule or the new anomaly rule has one or more traffic control settings to be applied to a traffic light at the intersection to at least one of prevent future occurrences of the anomaly or reduce a current impact of the anomaly on existing traffic at the intersection.

8. A traffic controller, comprising:

memory having computer-readable instructions stored therein; and

one or more processors configured to execute the computer-readable instructions to:

receive traffic data at an intersection;

determine statistics associated with traffic at the intersection based on the traffic data;

detect an anomaly in the traffic based on the statistics;

utilize an unmanned aerial vehicle (UAV) to determine a root cause of the anomaly; and

communicate an anomaly rule based on the root cause to a light controller to control traffic at the intersection.

9. The traffic controller of claim 8 , wherein the one or more processors are configured to execute the computer-readable instructions to determine the statistics by recording, for each of a plurality of time intervals and for each zone at the intersection, at least one of:

an average traffic flow rate for a corresponding zone; and

whether at least one object is detected in the corresponding zone.

10. The traffic controller of claim 9 , wherein the UAV is utilized if the average traffic flow rate is less than a threshold and no object is detected in the corresponding zone.

11. The traffic controller of claim 9 , wherein the anomaly is detected when the average traffic flow rate in a corresponding zone at the intersection is equal to or less than a threshold and no object is detected in the corresponding zone for at least two consecutive ones of the plurality of time intervals.

12. The traffic controller of claim 8 , wherein the one or more processors are configured to execute the computer-readable instructions to utilize the UAV by:

retrieving settings for the UAV;

launching the UAV to monitor one or more target zones to determine the root cause; and

determining if, in one or more of the target zones, a corresponding traffic flow rate is less than a threshold and at least one object is detected therein.

13. The traffic controller of claim 12 , wherein the one or more processors are configured to execute the computer-readable instructions to:

process image data of the one or more target zones for which the corresponding traffic flow rate is less than the threshold;

send the image data to a graphical user interface; and

determine the root cause of the anomaly based on at least one of a result of processing the image data or an input received from the graphical user interface in response to sending of the image data.

14. The traffic controller of claim 8 , wherein the one or more processors are configured to execute the computer-readable instructions to determine the anomaly rule by:

retrieving an existing anomaly rule from an anomaly rules database; or

generating a new anomaly rule, wherein

the existing anomaly rule or the new anomaly rule has one or more traffic control settings to be applied to a traffic light at the intersection to at least one of prevent future occurrences of the anomaly or reduce a current impact of the anomaly on existing traffic at the intersection.

15. The traffic controller of claim 8 , wherein the one or more processors are configured to execute the computer-readable instructions to determine the anomaly rule by:

retrieving an existing anomaly rule from an anomaly rules database; or

generating a new anomaly rule, wherein

the existing anomaly rule or the new anomaly rule has one or more traffic control settings to be applied to a traffic light at the intersection to at least one of prevent future occurrences of the anomaly or reduce a current impact of the anomaly on existing traffic at the intersection.

16. One or more non-transitory computer-readable medium having computer-readable instructions, which when executed by one or more processors, cause the one or more processors to:

receive traffic data at an intersection;

determine statistics associated with traffic at the intersection based on the traffic data;

detect an anomaly in the traffic based on the statistics;

utilize an unmanned aerial vehicle (UAV) to determine a root cause of the anomaly; and

communicate an anomaly rule based on the root cause to a light controller to control traffic at the intersection.

17. The one or more non-transitory computer-readable medium of claim 16 , wherein the execution of the computer-readable instructions by the one or more processors cause the one or more processors to determine the statistics by recording, for each of a plurality of time intervals and for each zone at the intersection, at least one of:

an average traffic flow rate for a corresponding zone; and

whether at least one object is detected in the corresponding zone.

18. The one or more non-transitory computer-readable medium of claim 17 , wherein the UAV is utilized if the average traffic flow rate is less than a threshold and no object is detected in the corresponding zone.

19. The one or more non-transitory computer-readable medium of claim 16 , wherein the execution of the computer-readable instructions by the one or more processors cause the one or more processors to utilize the UAV by:

retrieving settings for the UAV;

launching the UAV to monitor one or more target zones to determine the root cause; and

determining if, in one or more of the target zones, a corresponding traffic flow rate is less than a threshold and at least one object is detected therein.

20. The one or more non-transitory computer-readable medium of claim 19 , wherein the execution of the computer-readable instructions by the one or more processors cause the one or more processors to:

process image data of the one or more target zones for which the corresponding traffic flow rate is less than the threshold;

send the image data to a graphical user interface; and

determine the root cause of the anomaly based on at least one of a result of processing the image data or an input received from the graphical user interface in response to sending of the image data.