Methods, circuits, devices, systems and associated computer executable code for driver decision support

• US 10,639,995 B2
• Filed: 11/26/2018
• Issued: 05/05/2020
• Est. Priority Date: 11/10/2014
• Status: Active Grant

• Current Assignee: GT Gettaxi Systems Limited
• Original Assignee: GT GetTaxi Limited
• Inventors: Netzer, Yuval
• Primary Examiner(s): Soofi, Yazan A
• Application Number:
  US16/199,528
• Publication Number:
  US 20190092171A1
• Time in Patent Office: 526 Days
• Field of Search: 701 36
• CPC Class Codes: B60K 35/00 Instruments specially adapted for vehicles; Arrangement of instruments in or on vehicles
• CPC Class Codes: B60K 35/10 Input arrangements, i.e. from user to vehicle, associated with vehicle functions or specially adapted therefor
• CPC Class Codes: B60K 35/22 Display screens
• CPC Class Codes: B60K 35/85 Arrangements for transferring vehicle- or driver-related data
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• CPC Class Codes: H04W 4/024 Guidance services
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

Implementations are described for methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. An apparatus of one implementation includes processor circuitry communicably coupled to a display and a driver interface, the processor circuit to execute a recommendation engine to generate a recommended driver action comprising driving directions for a route, wherein the recommended driver action is optimized according to the operational parameter, wherein the recommendation is at least partially based upon a location signal indicating a current location of the driver, and a rendering unit to render upon the display a graphic representation of the recommended driver action, wherein said traffic flow model and said transportation demand model are dynamic and intermittently updated and said transportation demand module is segmented by location.

Claims

1. A mobile computing device comprising:

a display;

a driver interface adapted to receive a driver input of a driver associated with the mobile computing device, the driver input indicating one or more operational parameters for optimization of one or more of driver profit, fleet performance, average passenger wait time, or probability of arrival at a requested destination at a defined time; and

processor circuitry communicably coupled to the display and the driver interface, wherein the processor circuitry is to;

generate a first recommended driver action comprising driving directions for a route and a second recommended driver action, wherein the first recommended driver action and the second recommended driver action are optimized according to the one or more operational parameters, wherein the first recommended driver action and the second recommended driver action are at least partially based on a location signal indicating a current location of the driver;

determine a ranking of the first recommended driver action and the second recommended driver action; and

render on the display a graphic representation of the first recommended driver action and the second recommended driver action based on the ranking.

2. The mobile computing device of claim 1, wherein the first recommended driver action is also at least partially based on at least one of a live traffic feed indicating current traffic conditions within an area including the current location of the driver, a traffic flow model for the area, a transportation demand model of at least the area indicating a current demand for commercial transportation within the area, or a transportation supply model of the area indicating a current supply of commercial transportation within the area.

3. The mobile computing device of claim 1, wherein the optimization of the driver profit comprises optimizing profit per shift.

4. The mobile computing device of claim 1, wherein the optimization of the driver profit comprises optimizing profit of next fare.

5. The mobile computing device of claim 1, wherein the probability of arrival at the requested destination at the defined time comprises a first probability the driver arrives in a specific geographical region at the defined time.

6. The mobile computing device of claim 1, wherein the optimization of the fleet performance comprises optimizing profit of a fleet of commercial drivers.

7. The mobile computing device of claim 1, wherein the processor circuitry is further to calculate a second probability that a commercial driver is e-hailed at a given location.

8. The mobile computing device of claim 1, wherein the processor circuitry is further to calculate a third probability that a commercial driver is street hailed at a given location or navigating on a given route.

9. The mobile computing device of claim 8, wherein the processor circuitry is further to calculate, for each of a set of possible locations, a first probability the driver is hailed on route to each given location, aggregate a second probability of being e-hailed at each given location with the third probability the driver is street hailed on route to each given location into a fourth probability, and recommend an optimal location for the driver to drive to considering both of the first probability and the fourth probability.

10. A server computing device comprising:

memory; and

processor circuitry communicably coupled to the memory, the processor circuitry to;

generate a first recommended driver action and a second recommended driver action for a driver, wherein the first recommended driver action comprises driving directions for a route, wherein the first recommended driver action and the second recommended driver action are optimized according to one or more operational parameters indicated by the driver for optimization of one or more of driver profit, fleet performance, average passenger wait time, or probability of arrival at a requested destination at a defined time, and wherein the one or more operational parameters is at least partially based upon a location signal indicating a current location of the driver;

determine a ranking of the first recommended driver action and the second recommended driver action; and

transmit, to a client computing device of the driver, instructions to render a graphic representation of the first recommended driver action and the second recommended driver action based on the ranking at the client computing device.

11. The server computing device according to claim 10, wherein the memory stores the one or more operational parameters indicated by the driver in response to receiving the one or more operational parameters from the client computing device.

12. The server computing device according to claim 11, wherein the first recommended driver action is also at least partially based on data received from other mobile devices, a live traffic feed indicating current traffic conditions within an area including the current location of the driver, a traffic flow model for the area, a transportation demand model of at least the area indicating a current demand for commercial transportation within the area, or a transportation supply model of the area indicating a current supply of commercial transportation within the area.

13. The server computing device according to claim 11, wherein the optimization of the driver profit comprises optimizing at least one of profit per shift or profit of next fare.

14. The server computing device according to claim 11, wherein the processor circuitry is further to calculate a second probability the driver is e-hailed at a given location.

15. The server computing device according to claim 10, wherein the processor circuitry is further to calculate a third probability the driver is street hailed at a given location or cruising a given route.

16. The server computing device according to claim 15, wherein the processor circuitry is further to calculate first probability the driver is hailed on route to the given location, aggregate a second probability the driver is e-hailed at each given location with the third probability that the driver is hailed on route to each given location into a fourth probability, and recommend an optimal location for the driver to navigate to considering both the first probability and the fourth probability.

17. A method comprising:

receiving, by a processing device of a mobile computing device, a driver input of a driver associated with the mobile computing device, the driver input indicating one or more operational parameters for optimization of one or more of driver profit, fleet performance, average passenger wait time, or probability of arrival at a requested destination at a defined time;

generating, by the processing device, a first recommended driver action and a second recommended driver action, wherein the first recommended driver action comprises driving directions for a route, wherein generating the first recommended driver action and the second recommended driver action further comprises optimizing the first recommended driver action and the second recommended driver action according to the one or more operational parameters, wherein the first recommended driver action and the second recommended driver action are at least partially based upon a location signal indicating a current location of the driver;

determine a ranking of the first recommended driver action and the second recommended driver action; and

providing, via a user interface of the mobile computing device, a graphic representation of the first recommended driver action and the second recommended driver action based on the ranking.

18. The method of claim 17, wherein the first recommended driver action is further optimized at least partially based on at least one of a live traffic feed indicating current traffic conditions within an area including the current location of the driver, a traffic flow model for the area, a transportation demand model of at least the area indicating a current demand for commercial transportation within the area, or a transportation supply model of the area indicating a current supply of commercial transportation within the area.

19. The method of claim 17, wherein the one or more operational parameters are for the optimization of at least one of:

the driver profit by optimizing one or more of profit per shift or profit of next fare;

the probability of arrival at the requested destination at the defined time by optimizing a first probability that the driver arrives in a specific geographical region at the defined time;

or

the fleet performance by optimizing profit of a fleet of commercial drivers.

20. The method of claim 17, further comprising:

calculating, for each of a set of possible locations, a first probability the driver is hailed on route to each given location;

aggregating a second probability of being e-hailed at each given location with a third probability the driver is street hailed on route to each given location into a fourth probability; and

recommending an optimal location for the driver to drive to considering both of the first probability and the fourth probability.