USER TRIP LIBRARIES

• US 20170003137A1
• Filed: 06/30/2015
• Published: 01/05/2017
• Est. Priority Date: 06/30/2015
• Status: Abandoned Application

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Scofield, Christopher L.
• Application Number:
  US14/754,791
• Publication Number:
  US 20170003137A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 21/3476 using point of interest [POI] information, e.g. a route passing visible POIs
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/3691 Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G06Q 30/0266 based on the position of the vehicle
• CPC Class Codes: G08G 1/096888 where input information is obtained using learning systems, e.g. history databases

Abstract

One or more techniques and/or systems are provided for generating a trip library. Locational information, obtained from a device of a user (e.g., a mobile device, a wearable device, etc.), may be evaluated to identify trips traveled by the user and terminals traveled to by the user above a visitation frequency threshold (e.g., work, a favorite coffee shop, school, etc.). The trips and terminals may be evaluated to determined preferred trips that the user travels above a travel frequency threshold (e.g., routes that the user prefers to travel when going to a terminal, such as routine routes to a grocery store, work, etc.). A trip library, comprising the terminals, the preferred trips, and conditional likelihoods that he user will travel to a terminal given one or more conditions, may be created and used to preemptively provide the user with suggestions (e.g., notice of an event, coupon, alternative route, accident, etc.).

Claims

1. A method for generating a trip library for a user, comprising:

obtaining locational information from a device associated with a user;

evaluating the locational information to identify a set of trips traveled by the user;

evaluating the locational information to identify a set of terminals traveled to by the user;

evaluating the set of trips and the set of terminals to identify a set of preferred trips traveled by the user above a travel frequency threshold; and

generating a trip library based upon the set of terminals and the set of preferred trips.

2. The method of claim 1, the evaluating the locational information to identify a set of trips comprising:

evaluating the locational information to identify a first mode of transportation transition and a second mode of transportation transition;

identifying a trip start location of a trip based upon the first mode of transportation transition; and

identifying a trip end location of the trip based upon the second mode of transportation transition.

3. The method of claim 2, the evaluating the locational information to identify a first mode of transportation transition comprising:

identifying a change in velocity indicative of the user changing from a first mode of transportation to a second mode of transportation.

4. The method of claim 1, the evaluating the locational information to identify a set of terminals comprising:

clustering locational points within the locational information to identify a cluster indicative of a location corresponding to at least one of a trip start location or a trip end location; and

determining that the cluster is indicative of a terminal based upon the cluster comprising a threshold number of locational points having a locational distribution within a spatially compact threshold.

5. The method of claim 1, comprising:

determining a set of conditional likelihoods, for inclusion within the trip library, indicative of the user traveling to destination terminals of the set of terminals based upon the set of preferred trips.

6. The method of claim 1, the generating a trip library comprising:

storing, within the trip library for a terminal within the set of terminals, at least one of coordinates of the terminal, a mean time at which the terminal was entered or exited, a neighboring point of interest within a threshold distance of the terminal, a street address of the terminal, an event hosted at the terminal, or a description of the terminal.

7. The method of claim 1, the evaluating the set of trips and the set of terminals to identify a set of preferred trips comprising:

responsive to identify an occurrence of a trip between a first terminal and a second terminal exceeding a trip frequency threshold, determining that the trip is a preferred trip.

8. The method of claim 7, the determining that the trip is a preferred trip comprising:

determining that a first trip is an occurrence of the trip based upon a set of location points of the first trip corresponding to a threshold number of locational points of the trip.

9. The method of claim 5, the determining a set of conditional likelihoods comprising:

determining a first conditional likelihood as a probability that the user will travel to a destination given an occurrence of one or more route independent conditions.

10. The method of claim 9, a route independent condition comprising at least one of a time of day condition, a day of week condition, a holiday condition, a school in session condition, an event occurrence condition, a weather condition, a user mood condition, a user interest condition, a user social network post topic condition, a calendar entry topic condition, or a message topic condition.

11. The method of claim 9, the determining a first conditional likelihood comprising:

decomposing the first conditional likelihood based upon a probability that a condition is a target condition given the user traveling to the destination, an overall probability of the user visiting the destination, and a modelization factor corresponding to a frequency of going to the destination compared to a sum of frequencies for visiting possible destinations.

12. The method of claim 5, comprising:

utilizing a Bayes rule to compute a likelihood of the user visiting a terminal based upon the set of conditional likelihoods.

13. The method of claim 1, comprising:

identifying a preferred trip, within the trip library, corresponding to a terminal;

prompting the user to provide a description for the terminal; and

responsive to receiving the description, updating the trip library based upon the description.

14. The method of claim 1, comprising:

identifying a condition;

evaluating the trip library based upon the condition to identify a preferred trip; and

providing a recommendation associated with the preferred trip to the user.

15. The method of claim 14, comprising:

identifying supplement content corresponding to the preferred trip; and

providing the supplemental content to the user.

16. The method of claim 15, the supplemental content comprising at least one of an advertisement associated with a destination terminal of the preferred trip, weather information, travel time information, an incident along the preferred trip, an alternative route, a website, an image, a suggestion of a point of interest, or an event occurring at a location along the preferred trip.

17. A system for generating a trip library for a user, comprising:

a trip library generation component configured to;

obtain locational information from a device associated with a user;

evaluate the locational information to identify a set of trips traveled by the user;

evaluate the locational information to identify a set of terminals traveled to by the user;

evaluate the set of trips and the set of terminals to identify a set of preferred trips traveled by the user above a travel frequency threshold; and

generate a trip library based upon the set of terminals and the set of preferred trips; and

a route prediction component configured to;

identify a condition;

evaluate the trip library based upon the condition to identify a preferred trip; and

provide a recommendation associated with the preferred trip to the user.

18. The system of claim 17, the trip library generation component configured to:

determine a set of conditional likelihoods, for inclusion within the trip library, indicative of the user traveling to destination terminals of the set of terminals based upon the set of preferred trips.

19. The system of claim 17, the route prediction component configured to:

identify supplement content corresponding to the preferred trip, the supplemental content comprising at least one of an advertisement associated with a destination terminal of the preferred trip, weather information, travel time information, an incident along the preferred trip, an alternative route, a website, an image, a suggestion of a point of interest, or an event occurring at a location along the preferred trip; and

provide the supplemental content to the user.

20. A computer readable medium comprising instructions which when executed perform a method for generating a trip library for a user, comprising:

obtaining locational information from a device associated with a user;

evaluating the locational information to identify a set of trips traveled by the user;

evaluating the locational information to identify a set of terminals traveled to by the user;

evaluating the set of trips and the set of terminals to identify a set of preferred trips traveled by the user above a travel frequency threshold;

determining a set of conditional likelihoods indicative of the user traveling to destination terminals of the set of terminals based upon the set of preferred trips;

generating a trip library based upon the set of terminals, the set of preferred trips, and the set of conditional likelihoods;

identifying a condition;

evaluating the trip library based upon the condition to identify a preferred trip; and

providing a recommendation associated with the preferred trip to the user.