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