Continuously updatable computer-generated routes with continuously configurable virtual bus stops for passenger ride-sharing of a fleet of ride-sharing vehicles and computer transportation systems and computer-implemented methods for use thereof

• US 10,677,604 B1
• Filed: 12/14/2018
• Issued: 06/09/2020
• Est. Priority Date: 07/20/2015
• Status: Active Grant

• Current Assignee: Via Transportation Inc.
• Original Assignee: Via Transportation Inc.
• Inventors: Racah, Yaron, Sheba, Avishay, Shoval, Oren, Ramot, Daniel, Marcovitch, Shmulik
• Primary Examiner(s): Nguyen, Cuong H
• Application Number:
  US16/220,698
• Time in Patent Office: 543 Days
• Field of Search: 701400-410, 701412, 340991-994
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• 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 21/3423 Multimodal routing, i.e. combining two or more modes of transportation, where the modes can be any of, e.g. driving, walking, cycling, public transport
• CPC Class Codes: G01C 21/343 Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047
• CPC Class Codes: G01C 21/3438 Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
• CPC Class Codes: G01C 21/36 Input/output arrangements for on-board computers
• CPC Class Codes: G01C 21/3676 Overview of the route on the road map
• CPC Class Codes: G01S 19/39 the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: G08G 1/0137 for specific applications
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching

Abstract

In some embodiments, the present invention provides a computer-implemented transportation system which can include at least the following components: a specialized computer machine, including: a non-transient memory, electronically storing particular computer executable program code; a specifically programmed computer processor of the specialized computer machine of the computer-implemented transportation system that is configured to perform at least the following operations: electronically receiving, in real-time, via a computer network, a plurality of electronic riding requests from a plurality of electronic computing devices operated by a plurality of ride-sharing requesting passengers; where each electronic riding request from each ride-sharing requesting passenger includes: a passenger-requested origin point, and a passenger-requested destination point; for a particular electronic riding request, dynamically determining, in real-time, from a plurality of candidate vehicles an assigned vehicle for picking up the particular ride-sharing requesting passenger and a pair of assigned virtual pickup and dropoff bus stop tasks.

Claims

1. A system for routing a ridesharing vehicle, the system comprising:

memory for storing historic ridesharing demand data associated with a geographic locale;

at least one processor configured to access the memory and to;

receive ride requests of a plurality of users headed to differing destinations, wherein each ride request includes an origin point and a destination point;

receive current vehicle location data for a plurality of ridesharing vehicles within the geographic locale, where the current vehicle location data includes global positioning system (GPS) data generated by at least one GPS component of at least one electronic computing device associated with each ridesharing vehicle;

determine, based on the current vehicle location data and the received ride requests, a ridesharing vehicle to pick up at least one of the users;

select virtual bus stops for the ridesharing vehicle, including a first virtual bus stop for picking up the at least one user and a second virtual bus stop for dropping off the at least one user;

use the historic ridesharing demand data to identify at least one high demand area in the geographical locale;

select a route from the first virtual bus stop to the second virtual bus stop, wherein the selected route passes through the at least one identified high demand area; and

direct the ridesharing vehicle to travel along the selected route for picking up the at least one user at the first virtual bus stop and dropping off the at least one user at the second virtual bus stop.

3. The system of claim 1, wherein the at least one processor is further configured to:

receive an additional ride request of an additional user, wherein the additional ride request includes an origin point located at a walking distance from a high demand area; and

transmit instructions to the additional user to travel to a virtual pick-up bus stop located in the high demand area.

4. The system of claim 1, wherein the at least one processor is further configured to:

determine a vehicle occupancy of the ridesharing vehicle; and

when the vehicle occupancy exceeds a pre-determined threshold of passengers, select a new route from the first virtual bus stop to the second virtual bus stop, wherein the new route does not pass through the at least one identified high demand area.

5. The system of claim 4, wherein the at least one processor is further configured to:

identify a first route that passes through a central avenue and a second route that passes through a peripheral avenue;

when the vehicle occupancy is below the pre-determined number of passengers, select the first route as the new route even if the second route is estimated to be faster.

6. The system of claim 4, wherein the pre-determined threshold is at least three empty seats available.

7. The system of claim 1, wherein the at least one processor is further configured to:

use the historic ridesharing demand data to identify a plurality of high demand areas in the geographical locale; and

prioritize the plurality of high demand areas according to currently expected demand.

8. The system of claim 7, wherein the at least one processor is further configured to allocate ridesharing vehicles between different high demand areas based on the currently expected demand in each of the plurality of high demand areas.

9. The system of claim 1, wherein the at least one processor is further configured to:

prioritize vacant ridesharing vehicles according to a currently expected demand; and

direct the vacant ridesharing vehicles to virtual terminals according to the prioritization.

10. The system of claim 1, wherein the first virtual bus stop includes a virtual pick-up bus stop located at a first location differing from an origin point of a corresponding user, and the second virtual bus stop includes a virtual drop-off bus stop at a second location differing from a destination point of the corresponding user.

11. The system of claim 10, wherein the at least one processor is further configured to readjust, during a ride of the ridesharing vehicle, the second location of the virtual drop-off bus stop when the ridesharing vehicle is being assigned to an additional user.

12. The system of claim 10, wherein the at least one processor is further configured to select the virtual pick-up bus stop such that the first location is less than a threshold distance from the origin point of the corresponding user and the second location is less than the threshold distance from the destination point of the corresponding user.

13. The system of claim 12, wherein the threshold distance is, at least partially, determined in real-time based on at least one of a weather condition, an overall system demand, and a delay duration associated with the ridesharing vehicle.

14. The system of claim 1, wherein the at least one processor is further configured to:

dynamically assign ridesharing vehicles to pick up users in a manner that minimizes at least one of the following quality of service factors;

i) a first duration of time which each user spends in the ridesharing vehicle;

ii) a second duration of time which each user spends waiting for the ridesharing vehicle to arrive at a respective virtual bus stop;

iii) a third duration of time which each user spends walking to a respective virtual pick-up bus stop;

or

iv) a fourth duration of time which each user spends walking to a respective desired destination.

15. The system of claim 1, wherein the at least one processor is further configured to:

generate a first time-estimation for the ridesharing vehicle to arrive at the first virtual bus stop for picking up the at least one user,

continuously track location a current location of the ridesharing vehicle prior to arrival at the first virtual bus stop, to generate an updated time-estimation for the ridesharing vehicle to arrive at the first virtual bus stop for picking up the at least one user;

cancel the assignment of the ridesharing vehicle when the updated time-estimation differs from the first time-estimation by more than a predefined threshold; and

reassign another ridesharing vehicle to pick up the at least one user from the first virtual bus stop.

2. A method for routing a ridesharing vehicle, the method comprising:

accessing historic ridesharing demand data associated with a geographic locale;

receiving ride requests of a plurality of users headed to differing destinations, wherein each ride request includes an origin point and a destination point;

receive current vehicle location data for a plurality of ridesharing vehicles within a geographic locale, where the current vehicle location data includes global positioning system (GPS) data generated by at least one GPS component of at least one electronic computing device associated with each ridesharing vehicle;

determining, based on the current vehicle location data and the received ride requests, a ridesharing vehicle to pick up at least one of the users s;

selecting virtual bus stops for the ridesharing vehicle, including a first virtual bus stop for picking up the at least one user and a second virtual bus stop for dropping off the at least one user,

using the historic ridesharing demand data to identify at least one high demand area in the geographical locale;

selecting a route from the first virtual bus stop to the second virtual bus stop, wherein the selected route passes through the at least one identified high demand area; and

directing the ridesharing vehicle to travel along the selected route for picking up the at least one user at the first virtual bus stop and dropping off the at least one user at the second virtual bus stop.

16. The method of claim 2, further comprising:

prioritizing vacant ridesharing vehicles according to a currently expected demand; and

directing the vacant ridesharing vehicles to virtual terminals according to the prioritization.

17. The method of claim 2, further comprising:

determining a vehicle occupancy of the ridesharing vehicle; and

when the vehicle occupancy exceeds a pre-determined threshold of passengers, selecting a new route from the first virtual bus stop to the second virtual bus stop, wherein the new route does not pass through the at least one identified high demand area.

18. The method of claim 2, further comprising:

using the historic ridesharing demand data to identify a plurality of high demand areas in the geographical locale; and

prioritizing the plurality of high demand areas according to currently expected demand.

19. The method of claim 18, further comprising:

allocating ridesharing vehicles between different high demand areas, based on the currently expected demand in each of the plurality of high demand areas.