Real-time monitoring and diagnostic processing of traffic control data

• US 9,875,654 B2
• Filed: 01/27/2016
• Issued: 01/23/2018
• Est. Priority Date: 01/30/2015
• Status: Active Grant

• Current Assignee: Yunex GmbH
• Original Assignee: Siemens AG, Siemens Industry Incorporated (Siemens AG)
• Inventors: Aparicio Ojea, Juan L., Collum, Brian, Valdez, Andrew, Wehrwein, Bradley
• Primary Examiner(s): Ghulamali, Qutbuddin
• Application Number:
  US15/007,248
• Publication Number:
  US 20160225258A1
• Time in Patent Office: 727 Days
• Field of Search: 340907, 340911, 340912, 340915
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/07 Controlling traffic signals
• CPC Class Codes: G08G 1/097 Supervising of traffic control systems, e.g. by giving an alarm if two crossing streets have green light simultaneously

Abstract

A traffic control monitoring and abnormality determination system and associated methods are disclosed for receiving and analyzing traffic controller input/output data during a learning phase to determine a model indicative of normal or healthy operation of the traffic controller in regulating traffic flow at an intersection and receiving and evaluating additional traffic controller input/output data against the model during an evaluation phase to determine whether an abnormality exists in operation of the traffic controller. If an abnormality is detected during the evaluation phase, the system may initiate a corrective action to resolve the abnormality such as sending an alarm signal to a traffic controller to cause the traffic controller to alter an operating state to resolve the abnormality.

Claims

1. A method, comprising:

receiving, by a computer processor, first traffic controller data from a traffic controller configured to control traffic flow at an intersection, wherein the first traffic controller data is indicative of at least one of a first input to the traffic controller or a first output from the traffic controller;

determining, by the computer processor, first feature data based at least in part on the first traffic controller data, wherein the first feature data corresponds to one or more model features;

determining, by the computer processor and based at least in part on the first feature data, a model for the traffic flow at the intersection;

receiving, by the computer processor, second traffic controller data from the traffic controller, wherein the second traffic controller data is indicative of at least one of a second input to the traffic controller or a second output from the traffic controller;

determining, by the computer processor and based at least in part on the model and the second traffic controller data, presence of an abnormality in the traffic flow at the intersection, wherein the abnormality in the traffic flow at the intersection is indicative of an operational abnormality of the traffic controller, and wherein determining the presence of the abnormality comprises;

determining, by the computer processor, second feature data based at least in part on the second traffic controller data, wherein the second feature data corresponds to the one or more model features;

determining, by the computer processor, a metric indicative of a deviation between the second feature data and the model;

determining, by the computer processor, that the metric meets or exceeds a threshold value; and

determining, by the computer processor, that the abnormality is present based at least in part on determining that the metric meets or exceeds the threshold value; and

initiating, by the computer processor, an action to resolve the abnormality.

2. The method of claim 1, wherein initiating the action to resolve the abnormality comprises causing an alarm signal to be transmitted to the traffic controller to cause the traffic controller to adjust at least one of the second input or the second output to resolve the abnormality.

3. The method of claim 1, wherein the one or more model features comprise at least one of a time domain statistical feature or a frequency domain statistical feature.

4. The method of claim 1, further comprising:

receiving, by the computer processor, sensor data captured by one or more traffic sensors; and

determining, by the computer processor and based at least in part on the sensor data, an operating condition of the traffic flow at the intersection,

wherein determining the feature data comprises determining, by the computer processor, a respective coefficient to be applied to at least one model feature of the one or more model features based at least in part on the operating condition of the traffic flow at the intersection.

5. The method of claim 1, wherein the model is a first model, the method further comprising:

receiving, by the computer processor, first sensor data captured by one or more traffic sensors;

determining, by the computer processor and based at least in part on the first sensor data, a first operating condition of the traffic flow at the intersection, wherein the first traffic controller data and the first model correspond to the first operating condition;

receiving, by the computer processor, second sensor data captured by the one or more traffic sensors;

determining, by the computer processor and based at least in part on the second sensor data, a second operating condition of the traffic flow at the intersection, the second operating condition being different from the first operating condition;

receiving, by the computer processor, third traffic controller data from the traffic controller, wherein the third traffic controller data corresponds to the second sensor data;

determining, by the computer processor, third feature data based at least in part on the third traffic controller data, wherein the third feature data corresponds to the one or more model features; and

determining, by the computer processor and based at least in part on the third feature data, a second model for the traffic flow at the intersection, wherein the second model corresponds to the second operating condition.

6. A system, comprising:

at least one memory storing computer-executable instructions; and

at least one processor configured to access the at least one memory and execute the computer-executable instructions to;

receive first traffic controller data from a traffic controller configured to control traffic flow at an intersection, wherein the first traffic controller data is indicative of at least one of a first input to the traffic controller or a first output from the traffic controller;

determine first feature data based at least in part on the first traffic controller data, wherein the first feature data corresponds to one or more model features;

determine, based at least in part on the first feature data, a model for the traffic flow at the intersection;

receive second traffic controller data from the traffic controller, wherein the second traffic controller data is indicative of at least one of a second input to the traffic controller or a second output from the traffic controller;

determine, based at least in part on the model and the second traffic controller data, presence of an abnormality in the traffic flow at the intersection, wherein the abnormality in the traffic flow at the intersection is indicative of an operational abnormality of the traffic controller, and wherein the at least one processor is configured to determine the presence of the abnormality by executing the computer-executable instructions to;

determine second feature data based at least in part on the second traffic controller data, wherein the second feature data corresponds to the one or more model features;

determine a metric indicative of a deviation between the second feature data and the model;

determine that the metric meets or exceeds a threshold value; and

determine that the abnormality is present based at least in part on determining that the metric meets or exceeds the threshold value; and

initiate an action to resolve the abnormality.

7. The system of claim 6, wherein the at least one processor is configured to initiate the action to resolve the abnormality by executing the computer-executable instructions to cause an alarm signal to be transmitted to the traffic controller to cause the traffic controller to adjust at least one of the second input or the second output to resolve the abnormality.

8. The system of claim 6, wherein the one or more model features comprise at least one of a time domain statistical feature or a frequency domain statistical feature.

9. The system of claim 6, wherein the at least one processor is further configured to execute the computer-executable instructions to:

receive sensor data captured by one or more traffic sensors; and

determine, based at least in part on the sensor data, an operating condition of the traffic flow at the intersection, and

wherein the at least one processor is configured to determine the feature data by executing the computer-executable instructions to determine a respective coefficient to be applied to at least one model feature of the one or more model features based at least in part on the operating condition of the traffic flow at the intersection.

10. The system of claim 6, wherein the model is a first model, and wherein the at least one processor is further configured to execute the computer-executable instructions to:

receive first sensor data captured by one or more traffic sensors;

determine, based at least in part on the first sensor data, a first operating condition of the traffic flow at the intersection, wherein the first traffic controller data and the first model correspond to the first operating condition;

receive second sensor data captured by the one or more traffic sensors;

determine, based at least in part on the second sensor data, a second operating condition of the traffic flow at the intersection, the second operating condition being different from the first operating condition;

receive third traffic controller data from the traffic controller, wherein the third traffic controller data corresponds to the second sensor data;

determine third feature data based at least in part on the third traffic controller data, wherein the third feature data corresponds to the one or more model features; and

determine, based at least in part on the third feature data, a second model for the traffic flow at the intersection, wherein the second model corresponds to the second operating condition.

11. A computer program product comprising a non-transitory storage medium readable by a processing circuit, the storage medium storing instructions executable by the processing circuit to cause a method to be performed, the method comprising:

receiving, by a computer processor, first traffic controller data from a traffic controller configured to control traffic flow at an intersection, wherein the first traffic controller data is indicative of at least one of a first input to the traffic controller or a first output from the traffic controller;

determining first feature data based at least in part on the first traffic controller data, wherein the first feature data corresponds to one or more model features;

determining, based at least in part on the first feature data, a model for the traffic flow at the intersection;

receiving second traffic controller data from the traffic controller, wherein the second traffic controller data is indicative of at least one of a second input to the traffic controller or a second output from the traffic controller;

determining, based at least in part on the model and the second traffic controller data, presence of an abnormality in the traffic flow at the intersection, wherein the abnormality in the traffic flow at the intersection is indicative of an operational abnormality of the traffic controller, and wherein determining the presence of the abnormality comprises;

determining second feature data based at least in part on the second traffic controller data, wherein the second feature data corresponds to the one or more model features;

determining a metric indicative of a deviation between the second feature data and the model;

determining that the metric meets or exceeds a threshold value; and

determining that the abnormality is present based at least in part on determining that the metric meets or exceeds the threshold value; and

initiating an action to resolve the abnormality.

12. The computer program product of claim 11, wherein initiating the action to resolve the abnormality comprises causing an alarm signal to be transmitted to the traffic controller to cause the traffic controller to adjust at least one of the second input or the second output to resolve the abnormality.

13. The computer program product of claim 11, the method further comprising:

receiving, by the computer processor, sensor data captured by one or more traffic sensors; and

determining, by the computer processor and based at least in part on the sensor data, an operating condition of the traffic flow at the intersection,

wherein determining the feature data comprises determining, by the computer processor, a respective coefficient to be applied to at least one model feature of the one or more model features based at least in part on the operating condition of the traffic flow at the intersection.

14. The computer program product of claim 11, wherein the model is a first model, the method further comprising:

receiving, by the computer processor, first sensor data captured by one or more traffic sensors;

determining, by the computer processor and based at least in part on the first sensor data, a first operating condition of the traffic flow at the intersection, wherein the first traffic controller data and the first model correspond to the first operating condition;

receiving, by the computer processor, second sensor data captured by the one or more traffic sensors;

determining, by the computer processor and based at least in part on the second sensor data, a second operating condition of the traffic flow at the intersection, the second operating condition being different from the first operating condition;

receiving, by the computer processor, third traffic controller data from the traffic controller, wherein the third traffic controller data corresponds to the second sensor data;

determining, by the computer processor, third feature data based at least in part on the third traffic controller data, wherein the third feature data corresponds to the one or more model features; and

determining, by the computer processor and based at least in part on the third feature data, a second model for the traffic flow at the intersection, wherein the second model corresponds to the second operating condition.