FLEET ROUTING CONTROL SYSTEM AND METHOD

1 . A system, comprising:

a server computer comprising one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:

determine a plurality of routes and corresponding route schedules for a plurality of ride service requests;

assign a plurality of vehicles to service each one of the plurality of routes;

for each one of the plurality of routes, assign a vehicle among the plurality of vehicles to a driver to perform a route according to a route schedule to which the vehicle is assigned;

monitor the plurality of vehicles, the plurality of routes and the corresponding route schedules;

train an artificial intelligence engine associated with the server computer using historical data associated with one or more of the route schedule, the route, the vehicle, the driver, and past exceptions;

detect, using the trained artificial intelligence engine, an exception having a potential to impact the route schedule before the exception causes in a delay;

identify, using the trained artificial intelligence engine, a resolution to the exception based on analysis of the historical data, wherein the resolution reduces or eliminates the potential to impact the route schedule; and

proactively reduce or eliminate the potential to impact the route schedule by automatically implementing the resolution to the exception.

2 . The system of claim 1 , wherein the resolution includes automatically adjusting a remainder of the route schedule after the exception is detected when it is determined that the remainder of the route will be impacted by the exception.

3 . The system of claim 1 , wherein the instructions, when executed by the one or processors, cause the one or more processors to:

determine, using the trained artificial intelligence engine, a score indicating an impact of the exception on a remainder of the route schedule based on the analysis of the historical data, wherein the resolution to the exception includes taking a corrective action associated with the route or the route schedule when the score is above a threshold.

4 . The system of claim 1 , wherein the instructions, when executed by the one or processors, cause the one or more processors to:

determine, using the trained artificial intelligence engine, a score indicating an impact of the exception on a remainder of the route schedule based on the analysis of the historical data, wherein the resolution to the exception includes sending a warning to a device scheduling or monitoring the route when the score is below a threshold.

5 . The system of claim 1 , wherein the instructions, when executed by the one or processors, cause the one or more processors to:

identify the exception on a dispatch control center device using one or more sensory cues.

6 . The system of claim 5 , wherein the dispatch control center device is configured to display historic data associated with a selected route, vehicle, or driver.

7 . The system of claim 1 , further comprising:

a dispatch control center device configured to display information associated with one or more of the plurality of vehicles and one or more of the plurality of routes.

8 . The system of claim 1 , wherein the exception is based on one or more of the driver failing to clock in at a scheduled time, the vehicle departing a vehicle storage facility after the scheduled time, the vehicle arriving at a first stop on the route for the vehicle, the vehicle being late to a scheduled stop, or not having the driver assigned to the vehicle.

9 . The system of claim 1 , wherein automatically implementing the resolution is based on a time threshold for performing a ride service request according to the route schedule.

10 . The system of claim 1 , wherein an element of the historical data associated with one or more of the route schedule, the route, the vehicle, the driver, or the exception is associated with a coefficient indicative of an age of the element of the historical data, wherein the element of the historical data is weighed using the coefficient.

11 . The system of claim 1 , wherein the instructions, when executed by the one or processors, cause the one or more processors to:

monitor the plurality of vehicles, the plurality of routes and the corresponding route schedules to estimate a location of the plurality of vehicles at a given time;

receive real-time information from vehicle devices, wherein a vehicle device is associated with each one of the plurality of vehicles; and

continuously update the estimated location of the plurality of vehicles at the given time based on the real-time information received from the vehicle devices.

12 . A method, comprising:

determining, by a server computer, a plurality of routes and corresponding route schedules for a plurality of ride service requests;

assigning, by the server computer, a plurality of vehicles to service each one of the plurality of routes;

for each one of the plurality of routes, assigning, by the server computer, a vehicle among the plurality of vehicles to a driver to perform a route according to a route schedule to which the vehicle is assigned;

monitoring, by the server computer, the plurality of vehicles, the plurality of routes and the corresponding route schedules;

training, using the server computer, an artificial intelligence engine associated with the server computer using historical data associated with one or more of the route schedule, the route, the vehicle, the driver, and past exceptions;

detecting, using the trained artificial intelligence engine, an exception having a potential to impact the route schedule before the exception causes in a delay;

identifying, using the trained artificial intelligence engine, a resolution to the exception based on analysis of the historical data, wherein the resolution reduces or eliminates the potential to impact the route schedule; and

proactively reduce or eliminate the potential to impact the route schedule by automatically implementing the resolution to the exception.

13 . The method of claim 12 , wherein the resolution includes automatically adjusting a remainder of the route schedule after the exception is detected when it is determined that the remainder of the route will be impacted by the exception.

14 . The method of claim 12 , further comprising:

determining, using the trained artificial intelligence engine, a score indicating an impact of the exception on a remainder of the route schedule based on the analysis of the historical data, wherein the resolution to the exception includes taking a corrective action associated with the route or the route schedule when the score is above a threshold.

15 . The method of claim 12 , further comprising:

determining, using the trained artificial intelligence engine, a score indicating an impact of the exception on a remainder of the route schedule based on the analysis of the historical data, wherein the resolution to the exception includes sending a warning to a device scheduling or monitoring the route when the score is below a threshold.

16 . The method of claim 12 , further comprising:

identifying the exception on a dispatch control center device using one or more sensory cues, wherein the dispatch control center device is configured to display historic data associated with a selected route, vehicle, or driver.

17 . The method of claim 12 , wherein the exception is based on one or more of the driver failing to clock in at a scheduled time, the vehicle departing a vehicle storage facility after the scheduled time, the vehicle arriving at a first stop on the route for the vehicle, the vehicle being late to a scheduled stop, or not having the driver assigned to the vehicle.

18 . The method of claim 12 , wherein automatically implementing the resolution is based on a time threshold for performing a ride service request according to the route schedule.

19 . The method of claim 12 , further comprising:

monitoring the plurality of vehicles, the plurality of routes and the corresponding route schedules to estimate a location of the plurality of vehicles at a given time;

receiving real-time information from vehicle devices, wherein a vehicle device is associated with each one of the plurality of vehicles; and

continuously updating the estimated location of the plurality of vehicles at the given time based on the real-time information received from the vehicle devices.

20 . A non-transitory computer-readable medium storing instructions that, when executed on a server computer, cause the server computer to perform steps comprising:

determining a plurality of routes and corresponding route schedules for a plurality of ride service requests;

assigning a plurality of vehicles to service each one of the plurality of routes;

for each one of the plurality of routes, assigning a vehicle among the plurality of vehicles to a driver to perform a route according to a route schedule to which the vehicle is assigned;

monitoring the plurality of vehicles, the plurality of routes and the corresponding route schedules;

training, using the server computer, an artificial intelligence engine associated with the server computer using historical data associated with one or more of the route schedule, the route, the vehicle, the driver, and past exceptions;

detecting, using the trained artificial intelligence engine, an exception having a potential to impact the route schedule before the exception causes in a delay;

identifying, using the trained artificial intelligence engine, a resolution to the exception based on analysis of the historical data, wherein the resolution reduces or eliminates the potential to impact the route schedule; and

proactively reduce or eliminate the potential to impact the route schedule by automatically implementing the resolution to the exception.