LEARNING COMMON ROUTES AND AUTOMATIC GEOFENCING IN FLEET MANAGEMENT

• US 20180372503A1
• Filed: 05/03/2018
• Published: 12/27/2018
• Est. Priority Date: 06/22/2017
• Status: Active Grant

• Current Assignee: Aeris Communications Inc.
• Original Assignee: Aeris Communications Inc.
• Inventors: Bagchi, Anupam
• Granted Patent: US 10,648,823 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/343 Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G01C 21/3676 Overview of the route on the road map
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• 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 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

A computer-implemented method and system for providing a geo-fence are disclosed.

The method for providing a geo-fence includes collecting and storing route information for one or more mobile devices; analyzing the route information for the one or more mobile devices to determine optimum route; and creating the geo-fence around a route to be monitored based on the route information for the optimum route.

The system for providing a geo-fence includes one or more mobile devices, a user interface and a data processing system. The data processing system includes a learning database, wherein the learning database collects and stores route information for the one or more mobile device; and an analytics engine, wherein the analytics engine analyzes the route information to determine optimum route, and create the geo-fence around a route to be monitored based on the route information for the optimum route.

Claims

1. A computer-implemented method for providing a geo-fence, the method comprising:

collecting and storing route information for one or more mobile devices;

analyzing the route information for the one or more mobile devices to determine optimum route; and

creating the geo-fence around a route to be monitored based on the route information for the optimum route.

2. The computer-implemented method of claim 1, wherein the route information for one or more mobile devices comprises any one or more of start location of the route, destination location for the route, time of the day for the travel, day of the week for the travel, duration of the travel, distance covered during the travel.

3. The computer-implemented method of claim 1, wherein analyzing the route information to determine optimum route comprises:

grouping the collected data by common end-points for the routes for which the data is collected,

ranking the routes according to distance covered and time taken for the journey, and

determining the optimum route based on the ranking.

4. The computer-implemented method of claim 3, wherein analyzing the route information to determine optimum route further comprises determining the optimum route based on at least one of:

time of the day for the travel, day of the week for the travel and a combination thereof.

5. The computer-implemented method of claim 1, wherein creating the geo-fence around a route to be monitored comprises:

choosing a set of points along the route to be monitored;

sampling at least two points from the set of points to form a parameterized curve;

calculating parameterized points along the parameterized curve to act as anchor points for perpendiculars along the curve;

calculating two points along each perpendicular at a predetermined distance from the route on either direction; and

joining end points of the perpendiculars to form a polygon to create the geo-fence for the route to be monitored.

6. The computer-implemented method of claim 5, wherein sampling at least two points from the set of points to form a parameterized curve comprises sampling at least two points that are equally spaced in terms of any of time travelled, distance travelled and a combination thereof.

7. The computer-implemented method of claim 5, wherein the parameterized points along the parameterized curve are uniformly separated.

8. A system for providing a geo-fence, the system comprising one or more mobile devices, a user interface and a data processing system, wherein the data processing system further comprises:

a learning database, wherein the learning database collects and stores route information for the one or more mobile device; and

an analytics engine, wherein the analytics engine analyzes the route information of the one or more mobile devices to determine optimum route, and creates the geofence around a route to be monitored based on the route information for the optimum route.

9. The system of claim 8, wherein the route information for one or more mobile devices comprises any one or more of start location of the route, destination location for the route, time of the day for the travel, day of the week for the travel, duration of the travel, distance covered during the travel.

10. The system of claim 8, wherein analyzing the route information to determine optimum route comprises:

grouping the collected data by common end-points for the routes for which the data is collected,

ranking the routes according to distance covered and time taken for the journey, and

determining the optimum route based on the ranking.

11. The system of claim 10, wherein analyzing the route information to determine optimum route further comprises determining the optimum route based on at least one of:

time of the day for the travel, day of the week for the travel and a combination thereof.

12. The system of claim 8, wherein creating the geo-fence around a route to be monitored comprises:

choosing a set of points along the route to be monitored;

sampling at least two points from the set of points to form a parameterized curve;

calculating parameterized points along the parameterized curve to act as anchor points for perpendiculars along the curve;

calculating two points along each perpendicular at a predetermined distance from the route on either direction; and

joining end points of the perpendiculars to form a polygon to create the geo-fence for the route to be monitored.

13. The system of claim 12, wherein sampling at least two points from the set of points to form a parameterized curve comprises sampling at least two points that are equally spaced in terms of any of time travelled, distance travelled and a combination thereof.

14. The system of claim 12, wherein the parameterized points along the parameterized curve are uniformly separated.

15. A non-transitory computer-readable medium having executable instructions stored therein that, when executed, cause one or more processors corresponding to a system for providing a geo-fence, the system comprising on or more mobile devices, a user interface and a data processing system including a learning database and an analytics engine, to perform operations comprising:

collecting and storing route information for one or more mobile devices by the learning database;

analyzing the route information for the one or more mobile devices to determine optimum route, and creating the geo-fence around a route to be monitored based on the route information for the optimum route by the analytics engine.

16. The non-transitory computer-readable medium of claim 15, wherein the route information for one or more mobile devices comprises any one or more of start location of the route, destination location for the route, time of the day for the travel, day of the week for the travel, duration of the travel, distance covered during the travel.

17. The non-transitory computer-readable medium of claim 15, wherein analyzing the route information to determine optimum route comprises:

grouping the collected data by common end-points for the routes for which the data is collected,

ranking the routes according to distance covered and time taken for the journey, and

determining the optimum route based on the ranking.

18. The non-transitory computer-readable medium of claim 17, wherein analyzing the route information to determine optimum route further comprises determining the optimum route based on at least one of:

time of the day for the travel, day of the week for the travel and a combination thereof.

19. The non-transitory computer-readable medium of claim 15, wherein creating the geo-fence around a route to be monitored comprises:

choosing a set of points along the route to be monitored;

sampling at least two points from the set of points to form a parameterized curve;

calculating parameterized points along the parameterized curve to act as anchor points for perpendiculars along the curve;

calculating two points along each perpendicular at a predetermined distance from the route on either direction; and

joining end points of the perpendiculars to form a polygon to create the automatic geo-fence for the route to be monitored.

20. The non-transitory computer-readable medium of claim 19, wherein sampling at least two points from the set of points to form a parameterized curve comprises sampling at least two points that are equally spaced in terms of any of time travelled, distance travelled and a combination thereof.

21. The non-transitory computer-readable medium of claim 19, wherein the parameterized points along the parameterized curve are uniformly separated.