Constraint-based complex dynamic route sequencing route sequencing for multi-vehicle fleets

• US 10,354,217 B2
• Filed: 07/25/2017
• Issued: 07/16/2019
• Est. Priority Date: 03/11/2016
• Status: Active Grant

• Current Assignee: Route4Me, Inc.
• Original Assignee: Route4Me, Inc.
• Inventors: Khasis, Dan
• Primary Examiner(s): Nguyen, Tan Q
• Application Number:
  US15/658,496
• Publication Number:
  US 20170323249A1
• Time in Patent Office: 721 Days
• Field of Search: 701410, 701412, 701533, 701117
• 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: G01C 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• 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 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0287 involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/096811 where the route is computed offboard
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• 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

Methods and systems to dynamically and optimally sequence routes based on historical and real-time traffic conditions, and to predict anticipated traffic conditions along the dynamically generated route are disclosed. Route sequencing may be based on a set of predefined constraints, e.g., distance, time, time with traffic, or any objective cost function. The system of the present invention may be implemented in a vehicle fleet comprising one or more vehicles with one or more depots, or with no depots. An optimization server obtains real-time, historical and/or predicted future traffic, weather, hazard, and avoidance-zone data on road segments to generate a route, while staying within parameters and constraints set by an automatic machine learning process, an artificial intelligence program, or a human administrator. The platform may be coupled to sensors positioned on roads, e.g., speed radar or camera, and sensors positioned in vehicles, e.g., GPS system or on-board diagnostic sensor.

Claims

1. A machine-implemented method, comprising:

receiving for a segment of a road at least one of a traffic data, a weather data, a hazard data and an avoidance zone data,

wherein the at least one of a traffic data, a weather data, a hazard data, and an avoidance zone data comprise at least one of a real-time data, a historical data, and a predictive data,

wherein the historical data matches with a current circumstance;

determining a current traffic condition and a future traffic condition based on the at least one of a traffic data, a weather data, a hazard data, and an avoidance zone data;

sequencing one or more driving routes comprising a plurality of user vehicles and a plurality of destinations based on one of a shortest travel time, a shortest distance, a shortest distance based on the current traffic condition and the future traffic condition, and an objective cost function,

wherein the driving route obeys one or more predetermined constraints; and

generating one or more driving directions based on the sequenced one or more driving routes.

2. The method of claim 1, further comprising:

wherein the avoidance zone data comprises an avoidance zone, a traffic signal schedule, and a bridge schedule.

3. The method of claim 1, further comprising:

wherein the avoidance zone data comprises a driver restriction constraint, a vehicle-type restriction constraint and a prohibited maneuver constraint.

4. The method of claim 3, further comprising:

wherein the prohibited maneuver constraint comprises a left-turn restriction, a right-turn restriction, and a U-turn restriction.

5. The method of claim 3, further comprising:

wherein the prohibited maneuver constraint is determined by a country detection process.

6. The method of claim 1, further comprising:

wherein the one or more predetermined constraints comprise at least one of an operational constraint, an asset management constraint, an inventory constraint, a business constraint, a regulatory constraint, and a natural world constraint.

7. The method of claim 6, further comprising:

wherein the asset management constraint comprises a pre-approved road constraint and a vehicle capacity constraint.

8. The method of claim 6, further comprising:

wherein the inventory constraint comprises one or more persons.

9. The method of claim 8, further comprising:

wherein at least one destination is dynamically tracked through a mobile device for delivery of an inventory in an immediate vicinity of the destination.

10. The method of claim 9, further comprising:

wherein the plurality of destinations is not at fixed geospatial coordinate or physical location.

11. The method of claim 9, further comprising:

wherein the geospatial position of the plurality of destinations is dynamic.

12. The method of claim 8, further comprising:

wherein a position of the plurality of destinations is dynamically tracked through at least one position detection device, and

wherein the dynamic tracking of the position of the plurality of destinations is followed by repeating the steps of receiving data, predicting the current traffic condition and the future traffic condition, sequencing the driving route and generating the driving direction.

13. The method of claim 6, further comprising:

wherein the business constraint comprises a tracked user behavioral constraint, a customer behavior, a vehicle maneuver constraint, and an external machine constraint.

14. The method of claim 6, further comprising:

wherein the at least one of an operational constraint, an asset management constraint, an inventory constraint, a business constraint, a regulatory constraint, and a natural world constraint are set at predetermined priority levels when two or more constraints are obeyed.

15. The method of claim 6, further comprising:

wherein the inventory constraint comprises an inventory amount constraint, a package size constraint, and an inventory processing time constraint, and

wherein the inventory constraint is dynamic.

16. The method of claim 1, further comprising:

wherein the driving route is based on a future time that the driving route will be implemented.

17. The method of claim 1, further comprising:

determining an intersection point between the user vehicle and an inventory replenishment vehicle, and

wherein the inventory replenishment vehicle comprises one or more inventory for the user vehicle.

18. The method of claim 17, further comprising:

wherein the one or more inventory comprises a person inventory.

19. The method of claim 1, further comprising:

presenting one or more information to the user vehicle,

wherein the information is at least one of a data and a metadata about the driving route,

wherein the data and the metadata are based on a cost computation calculated between two destinations of the driving route, and

wherein the cost computation is based on the least one of a traffic data, a weather data, a hazard data and an avoidance zone data.

20. The method of claim 1, further comprising:

wherein the plurality of destinations comprises at least one of a customer location and a position detection device attached to an object.