Systems and methods for utilizing a machine learning model to identify public parking spaces and for providing notifications of available public parking spaces

1. A method, comprising:

receiving, by a device and from a plurality of vehicle devices associated with a plurality of vehicles, first vehicle data associated with engine off conditions;

processing, by the device and using a machine learning model, data identifying a geographical area associated with the plurality of the vehicles and the first vehicle data, to determine parking data identifying parking spaces in the geographical area,

wherein the processing comprises establishing a threshold value related to the first vehicle data associated with engine off conditions;

receiving, by the device and from the plurality of vehicle devices, second vehicle data associated with engine on conditions;

identifying, by the device and based on the second vehicle data and the parking data, available parking spaces in the geographical area; and

performing, by the device and based on identifying the available parking spaces, one or more actions.

2. The method of claim 1 , wherein the first vehicle data comprises at least one of:

data identifying engine off conditions,

data identifying locations during engine off conditions, or

data identifying durations of engine off conditions.

3. The method of claim 1 , further comprising:

receiving information associated with a location of another vehicle and a request to locate a parking space within the geographical area; and

identifying, based on the available parking spaces and the location of the other vehicle, one or more parking spaces of the available parking spaces.

4. The method of claim 1 , further comprising:

receiving a request to locate a parking space within the geographical area;

identifying one or more parking spaces of the available parking spaces; and

providing a user interface that includes directions to the one or more parking spaces.

5. The method of claim 1 , wherein processing the data identifying the geographical area associated with the plurality of the vehicles and the first vehicle data further comprises:

dividing the geographical area into clusters associated with particular dimensions; and

processing data identifying the clusters.

6. The method of claim 1 , wherein processing the data identifying the geographical area associated with the plurality of the vehicles and the first vehicle data further comprises:

dividing the geographical area into clusters associated with particular dimensions, and

processing data identifying the clusters; and

the method further comprising:

adjusting the particular dimensions of the clusters of the geographical area,

wherein adjusting the particular dimensions increases an accuracy of the parking data identifying parking spaces in the geographical area relative to the parking data associated with the particular dimensions.

7. The method of claim 1 , wherein establishing the threshold value related to the data associated with engine off conditions comprises:

establishing a threshold time period associated with durations of the engine off conditions;

establishing a threshold quantity of engine off conditions;

establishing a threshold quantity of vehicles, of the plurality of vehicles, associated with the engine off conditions; and

determining the parking data identifying the parking spaces in the geographical area based on the threshold time period, the threshold quantity of the engine off conditions, and the threshold quantity of the vehicles associated with the engine off conditions.

8. A device, comprising:

one or more processors configured to:

receive, from a plurality of vehicle devices associated with a plurality of vehicles, first vehicle data associated with engine off conditions;

process, by the device and using a machine learning model, data identifying a geographical area associated with the plurality of the vehicles and the first vehicle data, to determine parking data identifying parking spaces in the geographical area,

wherein the processing comprises establishing a threshold value related to the data associated with engine off conditions;

receive, from the plurality of vehicle devices, second vehicle data associated with engine on conditions;

identify, based on the second vehicle data and the parking data, available parking spaces in the geographical area; and

perform, based on identifying the available parking spaces, one or more actions.

9. The device of claim 8 , wherein the first vehicle data comprises at least one of:

data identifying engine off conditions,

data identifying locations during engine off conditions, or

data identifying durations of engine off conditions.

10. The device of claim 8 , wherein the one or more processors are further configured to:

receive information associated with a location of another vehicle and a request to locate a parking space within the geographical area; and

identify, based on the available parking spaces and the location of the other vehicle, one or more parking spaces of the available parking spaces.

11. The device of claim 8 , wherein the one or more processors are further configured to:

receive a request to locate a parking space within the geographical area;

identify one or more parking spaces of the available parking spaces; and

provide a user interface that includes directions to the one or more parking spaces.

12. The device of claim 8 , wherein the one or more processors, when processing the data identifying the geographical area associated with the plurality of the vehicles and the first vehicle data, are further configured to:

divide the geographical area into clusters associated with particular dimensions; and

process data identifying the clusters.

13. The device of claim 8 , wherein the one or more processors, when processing the data identifying the geographical area associated with the plurality of the vehicles and the first vehicle data, are further configured to:

divide the geographical area into clusters associated with particular dimensions, and

process data identifying the clusters; and

wherein the one or more processors are further configured to:

adjust the particular dimensions of the clusters of the geographical area,

wherein adjusting the particular dimensions increases an accuracy of the parking data identifying parking spaces in the geographical area relative to the parking data associated with the particular dimensions.

14. The device of claim 8 , wherein the one or more processors, when establishing the threshold value related to the data associated with engine off conditions, are configured to:

establish a threshold time period associated with durations of the engine off conditions;

establish a threshold quantity of engine off conditions;

establish a threshold quantity of vehicles, of the plurality of vehicles, associated with the engine off conditions; and

determine the parking data identifying the parking spaces in the geographical area based on the threshold time period, the threshold quantity of the engine off conditions, and the threshold quantity of the vehicles associated with the engine off conditions.

15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the device to:

receive, from a plurality of vehicle devices associated with a plurality of vehicles, first vehicle data associated with engine off conditions;

process, by the device and using a machine learning model, data identifying a geographical area associated with the plurality of the vehicles and the first vehicle data, to determine parking data identifying parking spaces in the geographical area,

wherein the processing comprises establishing a threshold value related to the data associated with engine off conditions;

receive, from the plurality of vehicle devices, second vehicle data associated with engine on conditions;

identify, based on the second vehicle data and the parking data, available parking spaces in the geographical area; and

perform, based on identifying the available parking spaces, one or more actions.

16. The non-transitory computer-readable medium of claim 15 , wherein the first vehicle data comprises at least one of:

data identifying engine off conditions,

data identifying locations during engine off conditions, or

data identifying durations of engine off conditions.

17. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:

receive information associated with a location of another vehicle and a request to locate a parking space within the geographical area; and

identify, based on the available parking spaces and the location of the other vehicle, one or more parking spaces of the available parking spaces.

18. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:

receive a request to locate a parking space within the geographical area;

identify one or more parking spaces of the available parking spaces; and

provide a user interface that includes directions to the one or more parking spaces.

19. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the device to process the data identifying the geographical area associated with the plurality of the vehicles and the first vehicle data, cause the device to:

divide the geographical area into clusters associated with particular dimensions; and

process data identifying the clusters.

20. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the device to establish the threshold value related to the data associated with engine off conditions, cause the device to:

establish a threshold time period associated with durations of the engine off conditions;

establish a threshold quantity of engine off conditions;

establish a threshold quantity of vehicles, of the plurality of vehicles, associated with the engine off conditions; and

determine the parking data identifying the parking spaces in the geographical area based on the threshold time period, the threshold quantity of the engine off conditions, and the threshold quantity of the vehicles associated with the engine off conditions.