SYSTEM AND METHOD FOR AUTOMATED IDENTIFICATION OF LOCATION TYPES FOR GEOFENCES

• US 20150077276A1
• Filed: 09/16/2013
• Published: 03/19/2015
• Est. Priority Date: 09/16/2013
• Status: Active Grant

• Current Assignee: Verizon Connect Development Limited (Verizon Communications Inc.)
• Original Assignee: Fleetmatics Ireland Limited (Verizon Communications Inc.)
• Inventors: Mitchell, Peter, Mocanu, Ovidiu
• Granted Patent: US 9,313,616 B2
• Time in Patent Office: Days
• US Class Current: 340/995.1
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/022 with dynamic range variability

Abstract

A system and method and system flow for processing GPS event data to identify frequent stop locations and geofences therefor and automatedly categorize them with location types.

Claims

1. A computer system including at least one computer processor and non-transitory computer readable storage medium or media including computer code and at least one storage device in which is stored GPS data for at least one vehicle the system comprising:

a memory including

a GPS event database including GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, and stored over a period of time;

the one or more processors programmed at least to

receive GPS event data transmitted from at least one GPS device, each GPS device associated with a vehicle, the event GPS event data including location information for the vehicle;

store the GPS event data in a GPS event database operatively coupled to at least one of the processors;

analyze the GPS event data to determine vehicle stops for at least one vehicle;

determine if the vehicle stops meets a location type identification criterion for a geofence; and

if the criterion is met, automatedly categorize the geofence.

3. The computer system of claim 1 or 2, wherein the one or more processors are further programmed at least to:

automatedly categorize the location type, the location type comprising a location type being selected from the group of;

an office location type, a depot location type, and a home location type.

4. The computer system of claim 3, wherein the one or more processors are further programmed at least to determine if the stops meet a location type identification criterion for the area by at least:

determining vehicle stop characteristics for the area; and

determining if the vehicle stop characteristics for the area meet a location type identification criterion.

5. The computer system of claim 4, wherein the one or more processors are further programmed at least to determine the vehicle stop characteristics by at least:

identify a total number of stops for at least one fleet of vehicles;

identify and sort the stops in order of the geofences with the highest average percentage of stops of the total stops for each geofences;

identify one or more of the geofences having the highest average percentage of stops as the highest ranked areas;

determine the variability of the stops for each of the highest ranked geofences;

identify, for each of the highest ranked areas, the number of stops that are last stop for a vehicle for that vehicle for a time period; and

identify, for each of the highest ranked areas, a unique vehicle count for the stops.

6. The computer system of claim 5, wherein the geofences are boundary shapes for automatedly identified frequent stop locations.

7. The computer system of claim 5, wherein the identification of a total number of stops for at least one fleet of vehicles is identified and sorted by a predefined geographical region.

8. The computer system of claim 5, wherein the one or more processors are further programmed to identify 7 to 10 geofences comprising the highest average percentage of stops.

9. The computer system of claim 6, wherein the determination of variability comprises calculating the standard deviation across the identified highest ranked geofences.

10. The computer system of claim 5, wherein the one or more processors are further programmed to determine if the vehicle stop characteristics for the geofence a location type identification criterion by at least:

selecting one or more of the identified highest ranked geofences for categorization; and

determining if the identified highest ranked geofences meet a threshold number of the location type identification criteria.

11. The computer system of claim 10, wherein the criteria comprise one or more of:

(1) a threshold coefficient of variation;

(2) a minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region;

(3) the number of stops for the geofence is a minimum number of stops for a predetermined time period

(4) at least one of the stops for the geofence is the last stop for a vehicle; and

(5) wherein a threshold number of stops are stops per unique vehicles.

12. The computer system of claim 11, wherein the vehicle stop characteristics for the geofence the geofence must meet a threshold number of the location type identification criteria.

13. The computer system of claim 12, wherein the threshold number location type identification criteria is in the range of 2 out of 5 criteria to 5 out of 5 criteria.

14. The computer system of claim 12, wherein the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is selected from the range of about at least 0.5 to at least about 0.7.

15. The computer system of claim 12 wherein the minimum percentage of the stops is at least 10% of the total stops for the number of stops for the fleet.

16. The computer system of claim 12 wherein the threshold number of unique stops is at least 30 stops per unique vehicle.

17. The computer system of claim 13, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system is configured to at least:

select only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type.

18. The computer system of claim 13, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the system is configured to at least:

select a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized and a depot location type or an office location type.

19. The computer system of claim 4, wherein the one or more processors are further programmed at least to determine if the geofence meets a home location type identification criterion for the geofence by at least:

identifying at least one geofence associated with a single vehicle;

determining, for each geofence associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period;

determining a duration of time for each of the number of stops over a time period are that are the last stop of the day;

and wherein the location type criterion comprises criteria including;

a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle;

a minimum number of vehicle stops in the geofence that are the last stop of the day; and

an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address.

2. The computer system of claim 2, wherein the geofence and wherein the boundary shape for the geofence is at least one of a boundary shape for an automatedly identified frequent stop location, a boundary shape for a user identified point of interest, and a boundary shape for predetermined point of interest.

20. A method comprising, in at least one computer and a computer readable non transitory storage medium or media including computer code:

receiving GPS event data transmitted from a plurality of GPS devices, each GPS device associated with a vehicle;

storing in GPS event database operatively coupled to at least one of the processors, the GPS event data;

analyzing the GPS event data to derive and record vehicle stops for at least one vehicle;

determine if the vehicle stops meets a location type identification criterion for a geofence; and

if the criterion is met, automatedly categorize the geofence.

21. The method of claim 20, wherein the geofence comprises at least one of a geofence for an automatedly identified frequent stop location, a boundary shape for a user identified point of interest, and a boundary shape for predetermined point of interest.

22. The method of claim 20 or 21, further comprising:

automatedly categorizing the location type, the location type comprising a location type being selected from the group of an office location type, a depot location type, and a home location type.

23. The method of claim 22, further comprising:

determining vehicle stop characteristics for the area; and

determining if the vehicle stop characteristics for the area meet a location type identification criterion.

24. The method of claim 23, further comprising:

identifying a total number of stops for at least one fleet of vehicles;

identifying and sorting the stops in order of the geofences with the highest average percentage of stops of the total stops for each geofences;

identifying one or more of the geofences having the highest average percentage of stops as the highest ranked areas;

determining the variability of the stops for each of the highest ranked geofences;

identifying, for each of the highest ranked areas, the number of stops that are last stop for a vehicle for that vehicle for a time period; and

identifying, for each of the highest ranked areas, a unique vehicle count for the stops.

25. The method of claim 24, wherein the geofences are boundary shapes for automatedly identified frequent stop locations.

26. The method of claim 24, wherein the identification of a total number of stops for at least one fleet of vehicles is identified and sorted by a predefined geographical region.

27. The method of claim 26, further comprising:

identifying 7 to 10 geofences comprising the highest average percentage of stops.

28. The method of claim 25, wherein the determination of variability comprises calculating the standard deviation across the identified highest ranked geofences.

29. The method of claim 24, further comprising:

selecting one or more of the identified highest ranked geofences for categorization; and

determining if the identified highest ranked geofences meet a threshold number of the location type identification criteria.

30. The method of claim 29, wherein the criteria comprise one or more of:

(1) a threshold coefficient of variation;

(2) a minimum percentage of the stops in the geofence of the total stops for the number of stops identified for the geographic region;

(3) the number of stops for the geofence is a minimum number of stops for a predetermined time period

(4) at least one of the stops for the geofence is the last stop for a vehicle; and

(5) wherein a threshold number of stops are stops per unique vehicles.

31. The method of claim 30, wherein the vehicle stop characteristics for the geofence must meet a threshold number of the location type identification criteria.

32. The method of claim 31, wherein the threshold number location type identification criteria is in the range of 2 out of 5 criteria to 5 out of 5 criteria.

33. The method of claim 32, wherein the number of geofences identified as having the highest number of stops is 7, and the threshold coefficient of variation is selected from at least about 0.5 to at least about 0.7

34. The method of claim 32 wherein the minimum percentage of the stops is at least 10% of the total stops for the number of stops for the fleet.

35. The method of claim 32 wherein the threshold number of unique stops is at least 30 stops per unique vehicle.

36. The method of claim 32, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises:

selecting only a first highest ranked geofence, whereby the first highest ranked is categorized as an office location type.

37. The method of claim 32, wherein for the selection of one or more of the highest ranked geofences having the highest number of stops for categorization the method further comprises:

selecting a first highest ranked geofence to a second highest ranked geofence, whereby the first highest ranked geofence is categorized as an office location type and the second highest ranked geofence is categorized and a depot location type or an office location type.

38. The method of claim 23, wherein the method further comprises determining if the geofence meets a home location type identification criterion for the geofence by at least:

identifying at least one geofence associated with a single vehicle;

determining, for each geofence associated with a single vehicle, a number of stops for the geofence, wherein the number of stops over a time period are the last stop of the day for a predetermined time period;

determining a duration of time for each of the number of stops over a time period are that are the last stop of the day;

and wherein the location type criterion comprises criteria including;

a minimum number of vehicle stops in the geofence that are the last stop of the day for the single vehicle;

a minimum number of vehicle stops in the geofence that are the last stop of the day; and

an address designation for a minimum percentage of stops within the geofence is designated as a qualified home address.