METHODS AND SYSTEMS FOR DETECTING AND VERIFYING ROUTE DEVIATIONS

• US 20180003516A1
• Filed: 08/09/2017
• Published: 01/04/2018
• Est. Priority Date: 03/11/2016
• Status: Active Grant

• Current Assignee: Route4Me, Inc.
• Original Assignee: Route4Me, Inc.
• Inventors: Khasis, Dan
• Granted Patent: US 9,952,056 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/3415 Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 10/063114 Status monitoring or status determination for a person or group
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 10/1097 Task assignment
• CPC Class Codes: G07C 5/02 Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: G08G 1/20 Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: G08G 1/207 with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries

Abstract

Systems and methods for geocoding coordinates of users, vehicles and destinations on a map, and to identify when a transit vehicle deviates from a system generated driving route are disclosed. As the transit vehicle travels along the route, its actual path of travel may be compared to an optimal driving route generated by the system to obtain route deviation data. The route deviation data and/or road network data may be filtered to remove data noise and further analyzed to build a profile of a driver operating a transit vehicle. A digital signature identifying at least one pattern of behavior and/or operating characteristic associated with a specific driver operating a vehicle is further developed, and may be used to allow a system administrator of a vehicle fleet to filter out, or filter for, specific driver types based on driving behaviors that may be suitable for a particular operation.

Claims

1. A machine-implemented method, comprising:

geocoding coordinates of at least one of a user, a vehicle, and a destination on a map;

tracking positioning data of the user, the vehicle, and the destination on the map to generate a tracked route;

comparing the tracked route to one or more generated routes;

detecting a deviation of the tracked route from a generated route; and

generating an alert to indicate that a route deviation has occurred.

2. The method of claim 1, further comprising:

wherein detecting the deviation comprises generating a pair of buffer perimeters surrounding a generated route;

comparing the tracked route to the pair of buffer perimeters; and

determining a route deviation occurrence based on the comparison of the vehicle'"'"'s location to the pair of buffer perimeters.

3. The method of claim 2, further comprising:

wherein a width of the pair of buffer perimeters is fixed, or

wherein a width of the pair of buffer perimeters is variable.

4. The method of claim 2, further comprising:

wherein generating a pair of buffer perimeter comprises overlaying one or more buffer zones on top of a generated route;

positioning the buffer zones'"'"' widths perpendicular to an average direction of a road segment of the generated route;

overlapping two or more buffer zones to indicate a change in direction of the generated route; and

calculating an overlap angle between a pair of overlapping adjacent buffer zones to determine a degree of change in direction of the generated route.

5. The method of claim 4, further comprising:

increasing a buffer zone frequency to increase an accuracy of a resultant pair of buffer perimeters to a generated route or;

decreasing a buffer zone length to increase an accuracy of a resultant pair of buffer perimeter to a generated route.

6. The method of claim 2, further comprising:

wherein generating a pair of buffer perimeters comprises overlapping one or more buffer strips on top of a generated route; and

positioning the buffer strips perpendicular to the generated route

7. The method of claim 6, further comprising:

wherein endpoints of each buffer strip corresponds to a width of a resultant pair of buffer perimeters, and

wherein a frequency of the buffer strips is directly proportional to an accuracy of the resultant pair of buffer perimeters to a generated route.

8. A data processing system, comprising:

a processor; and

a memory coupled to the processor, the memory storing instructions which when executed by the processor causes the processor to perform a method, comprising;

geocoding coordinates of at least one of a user, a vehicle, and a destination on a map;

tracking positioning data of the user, the vehicle, and the destination on the map to generate a tracked route,

wherein the tracking of the positioning data is performed at a predetermined time interval;

comparing the tracked route to one or more generated routes,

wherein the generated routes are prioritized based on at least one of a total distance, a total time, and an objective cost function,

detecting a deviation of the tracked route from a generated route; and

resequencing a generated route.

9. The system of claim 8, further comprising:

wherein detecting the deviation comprises capturing an image of a surrounding;

determining a geographic location of the surrounding;

comparing the location to one or more generated routes; and

determining a route deviation of a generated route.

10. The system of claim 9, further comprising:

determining whether the captured image is of a physical environment; and

verifying a captured weather data matches with a measured weather data.

11. The system of claim 9, further comprising:

wherein the captured image is a straight match with a model image of the geographic location, or

wherein the captured image is an offset match with a model image of the geographic location, and

wherein the offset match comprises a level of confidence based on a degree of shift of the captured image to the model image of the geographic location.

12. The system of claim 8, further comprising:

wherein detecting the deviation comprises capturing ambient sound waves of a surrounding;

matching the captured sound wave to a measured sound wave of a generated route; and

determining a route deviation occurrence of the tracked route from the generated route.

13. A machine-implemented method, comprising:

geocoding coordinates of at least one of a driver, a vehicle, and a destination on a map;

tracking positioning data of the driver, the vehicle, and the destination on the map to generate a tracked route;

comparing the tracked route to one or more generated routes;

detecting a deviation of the tracked route from a generated route; and

filtering a tracked route data and a generated route data.

14. The method of claim 13, further comprising:

comparing the tracked route to an average historical tracked route; and

constructing at least one new route leg based on the comparison of the tracked route to the average historical tracked route average historical tracked route

15. The method of claim 13, further comprising:

wherein filtering the tracked route data and the generated route data comprises determining whether a data corresponds to the tracked route or a generated route;

discarding the data if it does not correspond to the tracked route or the generated route; and

discarding the data if its header does not match a specified header.

16. The method of claim 13, further comprising:

generating a profile of a driver based on the tracked route data and a road network data;

determining one or more driving tendencies specific to the driver;

determining the driver'"'"'s value to an operation of a vehicle fleet; and

comparing two or more driver profiles based on the drivers'"'"' value to an operation or a driver-based parameter.

17. The method of claim 13, further comprising:

accessing the tracked route data and the generated route data;

predicting a future driving pattern based on a historical driving pattern of the diver; and

assigning a value score to the driver.

18. The method of claim 13, further comprising:

collecting vehicle operation data;

analyzing the operation data to identify a pattern of driving behavior;

generating a digital signature of the driver based on the pattern of driving behavior;

comparing a current pattern of driving behavior with a historical pattern of driving behavior; and

determining an identity of the driver based on the comparison.

19. The method of claim 13, further comprising:

identifying a plurality of drivers based on a characteristic match for an operation; and

filtering for a driver based on the characteristic match.

20. The method of claim 13, further comprising:

generating a graphical representation of a pattern of driving behavior;

comparing a current graphical representation to a historical graphical representation; and

determining an identity of the driver based on the comparison.