METHODS AND SYSTEMS FOR DETECTING AND VERIFYING ROUTE DEVIATIONS

• US 20200132479A1
• Filed: 12/30/2019
• Published: 04/30/2020
• Est. Priority Date: 03/11/2016
• Status: Abandoned Application

• Current Assignee: Route4Me, Inc.
• Original Assignee: Route4Me, Inc.
• Inventors: Khasis, Dan
• Application Number:
  US16/729,528
• Publication Number:
  US 20200132479A1
• 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 a user, a vehicle, a destination, or any combination thereof, on a map;

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

generating a pair of buffer perimeters surrounding a generated route;

comparing the tracked route to the pair of buffer perimeters;

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

wherein the pair of buffer perimeters encloses both sides of the generated route, and

wherein route deviation occurs if the vehicle'"'"'s location is disposed beyond the buffer perimeter'"'"'s width.

2. The method of claim 1, further comprising:

wherein a width of the pair of buffer perimeters is based on road data, and

wherein the road data comprises at least one of a structure, a highway, and a residential street.

3. The method of claim 2, further comprising:

wherein the structure comprises at least one of a bridge and an overpass, and

wherein a width of a pair of buffer perimeters corresponding to a bridge or an overpass corresponds to a width of the bridge or overpass.

4. The method of claim 1, further comprising:

wherein a midpoint of the pair of buffer perimeters align with a center position of a roadway of the generated route, or

wherein a midpoint of the pair of buffer perimeters do not align with a center position of a roadway of the generated route.

5. The method of claim 1, further comprising:

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

assigning a value score to the driver.

6. The method of claim 1, 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.

7. 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 a user, a vehicle, a destination, or any combination thereof, on a map;

tracking positioning data of the user, the vehicle, the destination, or any combination thereof, 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

resequencing a generated route.

8. The method of claim 7, further comprising:

drawing a line segment from a point of the tracked route and the corresponding spatial matching point of the generated route, and

wherein the line segment indicates an offset match.

9. The method of claim 7, further comprising:

constructing at least one new route leg based on the comparison of the deviation of the tracked route from the generated route.

10. The method of claim 7, 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.

11. The method of claim 7, 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.

12. The method of claim 7, 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; and

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

13. The method of claim 12, further comprising:

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

14. A machine-implemented method, comprising:

geocoding coordinates a driver, a vehicle, a destination, or any combination thereof, on a map;

tracking positioning data of the driver, the vehicle, the destination, or any combination thereof, 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.

15. The method of claim 14, further comprising:

determining an offset angle relative to an X-axis, and

wherein the offset angle indicates a directional position of the offset.

16. The method of claim 15, further comprising:

correcting a map shift using the directional position of the offset.

17. The method of claim 14, further comprising:

determining a positive value or negative value of a deviation occurrence to an operation.

18. The method of claim 14, further comprising:

assigning a deviation score to the driver, and

wherein an acceptable deviation score for each driver is based on a driver'"'"'s profile.

19. The method of claim 18, further comprising:

wherein the driver'"'"'s profile comprises the driver'"'"'s road knowledge data.

20. The method of claim 14, 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.