CARPOOL FINDER ASSISTANCE

• US 20160025507A1
• Filed: 07/25/2014
• Published: 01/28/2016
• Est. Priority Date: 07/25/2014
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: BAI, FAN, GRIMM, DONALD K., NIEMAN, LEONARD C.
• Granted Patent: US 9,612,127 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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: G06Q 10/02 Reservations, e.g. for tickets, services or events

Abstract

A method of determining ride share compatibility. Vehicle data acquisition devices are employed to collect user attribute information relating to a travel route and locations traveled by the operator. The attribute information includes regularity data, frequency data, and duration data. A regression analysis is applied by a processor for using the regularity data, the frequency data, and the duration data, for identifying an importance probability of each of the locations visited by the operator. A match is determined between the operator and a potential travel partner traveling to locations in proximity to the locations traveled by the operator.

Claims

1. A method of determining ride share compatibility, the method comprising the steps of:

employing vehicle data acquisition devices to collect user attribute information relating to a travel route and locations traveled by the operator, the attribute information including regularity data, frequency data, and duration data;

applying a regression analysis by a processor for using the regularity data, the frequency data, and the duration data, for identifying an importance probability of each of the locations visited by the operator;

determining a match between the operator and a potential travel partner traveling to locations in proximity to the locations traveled by the operator.

2. The method of claim 1 wherein the regularity data includes a number of days a respective location is visited in one week.

3. The method of claim 1 wherein the frequency data includes a number of times that a respective location is visited in one week.

4. The method of claim 1 wherein the duration data includes a length of time that the user stays at a respective location during one visit.

5. The method of claim 4 wherein the duration data for a respective location is an average for a week.

6. The method of claim 1 wherein the importance probability for each location determined by applying regression analysis is represented by the following formula:

7. The method of claim 6 wherein a respective location having a highest determined importance probability among each of the determined importance probabilities between the hours of 8 pm to 6 am during a weekday is determined to be a home location of the user.

8. The method of claim 6 wherein a respective location having a highest determined importance probability among each of the determined importance probabilities between the hours of 9 am to 5 pm during a weekday is determined to be a work location of the user.

9. The method of claim 1 wherein the vehicle data acquisition devices to autonomously collect user attribute information.

10. The method of claim 1 wherein the user manually enters the user attribute information to the vehicle data acquisition devices.

11. The method of claim 1 the vehicle trace data relating to historical information derived from a group that includes at least one of operator detection information, driving route information, operator driving behavior information, media accessory usage information, and multi-media content usage information.

12. The method of claim 1 wherein determining a match between user and the potential travel partner includes evaluating matching behaviors.

13. The method of claim 12 wherein evaluating matching behaviors includes evaluating a distance difference between the location traveled to by the user and the location traveled to by the potential partner, wherein the distance difference is represented by the formula:

D(m, n)=d_home(m, n)+d_work(m,n).

14. The method of claim 13 wherein evaluating matching behaviors includes evaluating a time difference between the location traveled to by the user and the location traveled to by the potential partner, wherein the time difference is represented by the formula:

τ

(m, n)=max(|t_{leave H}(m)−

t_{leave H}(n)|, |t_{leave W}(m)−

t_{leave W})(n)|).

15. The method of claim 14 wherein evaluating matching behaviors includes evaluating a time variance between the location traveled to by the user and the location traveled to by the potential partner, wherein the time variance is represented by the formula:

σ

(m, n)=max(|σ

_{leave H}(m)−

t_{leave H}(n)|, |σ

_{leave W}(m)−

σ

_{leave W}(n)|).

16. The method of claim 15 wherein a user selects maximum tolerances relating to the distance difference, time difference, and time variance for identifying a matching potential travel partner.

17. The method of claim 1 wherein evaluating matching behaviors between the user and the potential travel partner utilizes social network information.

18. The method of claim 17 wherein evaluating social network information includes direct connections within a social media group.

19. The method of claim 17 wherein evaluating social network information includes indirect connections within a social media group.

20. The method of claim 17 wherein indirect connections within a social media group utilizes connections within a predetermined number of hops between the user and the potential traveling partner.

21. The method of claim 1 wherein evaluating matching behaviors between the user and the potential travel partner utilizes vehicle similarity information.

22. The method of claim 1 wherein determining a match between user and the potential travel partner includes evaluating behavior similarities between the user and the potential travel partner.

23. The method of claim 22 wherein a user profile is autonomously generated over time as a function of the behaviors of the user.

24. The method of claim 23 wherein the behaviors of the user include driving behaviors.

25. The method of claim 17 wherein the behaviors of the user include radio listening behaviors.

26. The method of claim 17 wherein the behaviors of the user include air conditioning behaviors.

27. The method of claim 17 wherein the similarity between the user and the potential carpool partner is determined by the following formula: