Complex dynamic route sequencing for multi-vehicle fleets using traffic and real-world constraints

• US 9,792,575 B2
• Filed: 03/11/2017
• Issued: 10/17/2017
• 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 QUANG
• Application Number:
  US15/456,521
• Publication Number:
  US 20170262790A1
• Time in Patent Office: 220 Days
• Field of Search: 701410, 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 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;

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,

predicting 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, shortest distance, and shortest distance based on the current traffic condition and the future traffic condition, and an objective cost function;

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

splitting a plurality of driving directions among a plurality of vehicles into equal distributions based on one of an amount of destination, a travel distance, and a travel time; and

assigning a driving direction to a user vehicle of a plurality of user vehicles based on one or more rules.

2. The system of claim 1, further comprising:

determining a location of a fueling station based on at least one of a vehicle type, a travel distance, a travel time, a travel cost, a fueling time, a fuel cost, and a current fuel capacity, and

wherein the determining of a location of a fueling station is based on a machine learning process.

3. The system of claim 2, further comprising:

inserting a location of a fueling station closest to a next destination of the driving route into the driving route, and

wherein inserting the location of a fueling station 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.

4. The system 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.

5. The system of claim 4, further comprising:

wherein the one or more inventory comprises a person inventory.

6. The system of claim 4, further comprising:

wherein a position of the user vehicle and a position of the inventory replenishment vehicle are dynamically tracked through at least one position detection device, and

wherein the dynamic tracking of the position of the user vehicle and the position of the inventory replenishment vehicle 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.

7. The system 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.

8. The system of claim 1, further comprising:

modifying the driving route,

wherein modifying the driving route comprises at least one of an adding a destination and a removing a destination,

wherein modifying the driving route occurs while the driving route is in progress, and

wherein modifying the driving route 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.