System and method of adaptive traffic management at an intersection
A traffic control system and a method of automatic zone creation and modification for a smart traffic camera to be used in adaptive traffic management at an intersection are disclosed. One aspect of the present disclosure is a method including applying default zone parameters to define detection zones at one or more sensors installed at an intersection, the detection zones being used by the one or more sensors for monitoring and detecting traffic conditions at the intersection; determining a current vehicular traffic flow rate and a current pedestrian traffic flow rate at the intersection; determining if a triggering condition for adjusting one or more of the default zone parameters; and adjusting the one or more of the default zone parameters if the triggering condition is met.
1. A method comprising:
applying default zone parameters to define detection zones at one or more sensors installed at an intersection, the detection zones being used by the one or more sensors for monitoring and detecting traffic conditions at the intersection;
determining a current pedestrian traffic flow rate at the intersection;
determining if a triggering condition for adjusting one or more of the default zone parameters, wherein the triggering condition is affected by the current pedestrian traffic flow rate; and
adjusting the one or more of the default zone parameters if the triggering condition is met to dynamically change size or shape of the detection zones, wherein the adjustment of the one or more of the default zone parameters include:
querying a third party database for a plurality of factors to determine a rule using a machine learning algorithm, wherein:
the plurality of factors include pedestrian information, device information associated with the pedestrian information, and event information within a certain distance from the intersection, and
the rule is determined based on correlations between the current pedestrian traffic flow rate and a current vehicular traffic flow rate at the intersection; and
enlarging or reducing the one or more of the default zone parameters based on the rule determined using the plurality of factors from the third party database.
2. The method of claim 1 , wherein the default zone parameters are manually specified using a graphical user interface.
3. The method of claim 1 , wherein:
the triggering condition includes a pedestrian threshold and a vehicular threshold, and
the triggering condition is met when the current vehicular traffic flow rate is greater than the vehicular threshold and the current pedestrian traffic flow rate is greater than the pedestrian threshold.
4. The method of claim 1 , further comprising:
querying a pedestrian database to determine if a pedestrian rule exists if the triggering condition is met, wherein the one or more of the default zone parameters are adjusted in the pedestrian rule exists.
5. The method of claim 4 , wherein the pedestrian rule identifies one or more alternative parameters for one or more of the detection zones at the intersection.
6. The method of claim 5 , further comprising:
querying a zone setting database to retrieve alternative parameters for adjusting the one or more of the default zone parameters, and
adjusting the one or more of the default zone parameters using the alternative parameters.
7. The method of claim 4 , wherein the pedestrian database includes pedestrian rules created using a machine-learning algorithm.
8. A controller comprising:
memory having computer-readable instructions stored therein; and
one or more processors configured to execute the computer-readable instructions to:
apply default zone parameters to define detection zones at one or more sensors installed at an intersection, the detection zones being used by the one or more sensors for monitoring and detecting traffic conditions at the intersection;
determine a current pedestrian traffic flow rate at the intersection;
determine if a triggering condition for adjusting one or more of the default zone parameters, wherein the triggering condition is affected by the current pedestrian traffic flow rate; and
adjust the one or more of the default zone parameters if the triggering condition is met to dynamically change size or shape of the detection zones, wherein the adjustment of the one or more of the default zone parameters include:
query a third party database for a plurality of factors to determine a rule using a machine learning algorithm, wherein:
the plurality of factors include pedestrian information, device information associated with the pedestrian information, and event information within a certain distance from the intersection, and
the rule is determined based on correlations between the current pedestrian traffic flow rate and a current vehicular traffic flow rate at the intersection; and
enlarge or reduce the one or more of the default zone parameters based on the rule determined using the plurality of factors from the third party database.
9. The controller of claim 8 , wherein the default zone parameters are manually specified using a graphical user interface.
10. The controller of claim 8 , wherein
the triggering condition includes a pedestrian threshold and a vehicular threshold, and
the triggering condition is met when the current vehicular traffic flow rate is greater than the vehicular threshold and the current pedestrian traffic flow rate is greater than the pedestrian threshold.
11. The controller of claim 8 , wherein the one or more processors are configured to execute the computer-readable instructions to query a pedestrian database to determine if a pedestrian rule exists if the triggering condition is met, wherein the one or more of the default zone parameters are adjusted in the pedestrian rule exists.
12. The controller of claim 11 , wherein the pedestrian rule identifies one or more alternative parameters for one or more of the detection zones at the intersection.
13. The controller of claim 12 , wherein the one or more processors are configured to execute the computer-readable instructions to:
query a zone setting database to retrieve alternative parameters for adjusting the one or more of the default zone parameters, and
adjust the one or more of the default zone parameters using the alternative parameters.
14. The controller of claim 11 , wherein the pedestrian database includes pedestrian rules created using a machine-learning algorithm.
15. One or more non-transitory computer-readable medium having computer-readable instructions stored therein, which when executed by one or more processors of a controller, configure the controller to:
apply default zone parameters to define detection zones at one or more sensors installed at an intersection, the detection zones being used by the one or more sensors for monitoring and detecting traffic conditions at the intersection;
determine a current pedestrian flow rate at the intersection;
determine a current vehicular traffic flow rate and a current pedestrian traffic flow rate at the intersection;
determine if a triggering condition for adjusting one or more of the default zone parameters, wherein the triggering condition is affected by the current pedestrian traffic flow rate; and
adjust the one or more of the default zone parameters if the triggering condition is met to dynamically change size or shape of the detection zones, wherein the adjustment of the one or more of the default zone parameters include:
query a third party database for a plurality of factors to determine a rule using a machine learning algorithm, wherein:
the plurality of factors include pedestrian information, device information associated with the pedestrian information, and event information within a certain distance from the intersection, and
the rule is determined based on correlations between the current pedestrian traffic flow rate and the current vehicular traffic flow rate at the intersection; and
enlarge or reduce the one or more of the default zone parameters are enlarged based on the rule determined using the plurality of factors from the third party database.
16. The one or more non-transitory computer-readable medium of claim 15 , wherein the default zone parameters are manually specified using a graphical user interface.
17. The one or more non-transitory computer-readable medium of claim 15 , wherein
the triggering condition includes a pedestrian threshold and a vehicular threshold, and
the triggering condition is met when the current vehicular traffic flow rate is greater than the vehicular threshold and the current pedestrian traffic flow rate is greater than the pedestrian threshold.
18. The one or more non-transitory computer-readable medium of claim 15 , wherein the one or more processors are configured to execute the computer-readable instructions to query a pedestrian database to determine if a pedestrian rule exists if the triggering condition is met, wherein the one or more of the default zone parameters are adjusted in the pedestrian rule exists.
19. The one or more non-transitory computer-readable medium of claim 18 , wherein the pedestrian rule identifies one or more alternative parameters for one or more of the detection zones at the intersection.
20. The one or more non-transitory computer-readable medium of claim 19 , wherein the one or more processors are configured to execute the computer-readable instructions to:
query a zone setting database to retrieve alternative parameters for adjusting the one or more of the default zone parameters, and
adjust the one or more of the default zone parameters using the alternative parameters.