METHOD AND APPARATUS FOR PROVIDING PERSONALIZED ROUTING BASED ON USER ROUTING BEHAVIORS

• US 20160341564A1
• Filed: 05/22/2015
• Published: 11/24/2016
• Est. Priority Date: 05/22/2015
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: CHENG, Wanli
• Granted Patent: US 9,778,060 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• 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 21/3641 Personalized guidance, e.g. limited guidance on previously travelled routes

Abstract

An approach is provided for determining one or more previous behaviors made by at least one user traveling at least one calculated route. The one or more previous behaviors include, at least in part, one or more deviations from, one or more matches on, or a combination thereof for the at least one calculated route. The approach involves determining one or more predictor values for one or more predictors associated with the one or more previous behaviors. The approach also involves causing, at least in part, a creation of at least one predictive model based, at least in part, on the one or more predictor values. The at least one predictive model is used to predict one or more potential behaviors by the at least one user while traveling the at least one calculated route, at least one other route, or a combination thereof.

Claims

1. A method comprising:

determining one or more previous behaviors made by at least one user traveling at least one calculated route, wherein the one or more previous behaviors include, at least in part, one or more deviations from, one or more matches on, or a combination thereof for the at least one calculated route;

determining one or more predictor values for one or more predictors associated with the one or more previous behaviors; and

causing, at least in part, a creation of at least one predictive model based, at least in part, on the one or more predictor values,

wherein the at least one predictive model is used to predict one or more potential behaviors by the at least one user while traveling the at least one calculated route, at least one other route, or a combination thereof.

2. A method of claim 1, wherein the one or more predictor values include, at least in part, personalized data for the at least one user, the method further comprising:

causing, at least in part, a personalization of the at least one predictive model to the at least one user based, at least in part, on the personalized data.

3. A method of claim 2, further comprising:

determining weighting information for the one or more predictors based, at least in part, on the personalized data,

wherein the personalization of the at least one predictive model is further based, at least in part, on the weighting information.

4. A method of claim 1, further comprising:

receiving a request by the at least one user to generate at least one subsequent calculated route; and

processing and/or facilitating a processing of the at least one subsequent calculated route using the at least one predictive model to determine one or more predicted behaviors for the at least one user while traveling the at least one subsequent calculated route.

5. A method of claim 4, further comprising:

causing, at least in part, a personalization of navigation guidance information to present to the at least one user while traveling the at least one subsequent calculated route based, at least in part, on the one or more predicted behaviors.

6. A method of claim 5, wherein the personalization of the navigation guidance information includes, at least part, varying a type of the navigation guidance information, a frequency of the navigation guidance information, a timing for presenting the navigation guidance information, or a combination thereof.

7. A method of claim 1, further comprising:

causing, at least in part, an application at least one supervised feature selection mechanism to classify the one or more predictors as useful for prediction,

wherein the creation of the at least one predictive model is based, at least in part, on the one or more predictor values associated with the one or more predictors classified as useful for prediction.

8. A method of claim 1, further comprising:

determining predictive accuracy information for the at least one predictive model; and

causing, at least in part, an association of the at least one predictive model with at least one profile of the at least one user if the predictive accuracy information is above at least one threshold value.

9. A method of claim 1, further comprising:

determining the one or more deviations, the one or more matches, or a combination thereof by comparing at least one recorded route associated with the at least one user against the at least one calculated route.

10. A method of claim 1, wherein the one or more predictors include, at least in part, a route complexity predictor, a behavior commonality predictor, a route familiarity predictor, a routing sequence predictor, a user behavior predictor, a traffic predictor, a weather predictor, or a combination thereof.

11. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs,

the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,

determine one or more previous behaviors made by at least one user traveling at least one calculated route, wherein the one or more previous behaviors include, at least in part, one or more deviations from, one or more matches on, or a combination thereof for the at least one calculated route;

determine one or more predictor values for one or more predictors associated with the one or more previous behaviors; and

cause, at least in part, a creation of at least one predictive model based, at least in part, on the one or more predictor values,

wherein the at least one predictive model is used to predict one or more potential behaviors by the at least one user while traveling the at least one calculated route, at least one other route, or a combination thereof.

12. An apparatus of claim 11, wherein the one or more predictor values include, at least in part, personalized data for the at least one user, the method further comprising:

causing, at least in part, a personalization of the at least one predictive model to the at least one user based, at least in part, on the personalized data.

13. An apparatus of claim 12, further comprising:

determine weighting information for the one or more predictors based, at least in part, on the personalized data,

wherein the personalization of the at least one predictive model is further based, at least in part, on the weighting information.

14. An apparatus of claim 11, further comprising:

receive a request by the at least one user to generate at least one subsequent calculated route; and

process and/or facilitate a processing of the at least one subsequent calculated route using the at least one predictive model to determine one or more predicted behaviors for the at least one user while traveling the at least one subsequent calculated route.

15. An apparatus of claim 14, further comprising:

cause, at least in part, a personalization of navigation guidance information to present to the at least one user while traveling the at least one subsequent calculated route based, at least in part, on the one or more predicted behaviors.

16. An apparatus of claim 11, further comprising:

cause, at least in part, an application at least one supervised feature selection mechanism to classify the one or more predictors as useful for prediction,

wherein the creation of the at least one predictive model is based, at least in part, on the one or more predictor values associated with the one or more predictors classified as useful for prediction.

17. An apparatus of claim 11, further comprising:

determine predictive accuracy information for the at least one predictive model; and

cause, at least in part, an association of the at least one predictive model with at least one profile of the at least one user if the predictive accuracy information is above at least one threshold value.

18. A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform the following steps:

determining one or more previous behaviors made by at least one user traveling at least one calculated route, wherein the one or more previous behaviors include, at least in part, one or more deviations from, one or more matches on, or a combination thereof for the at least one calculated route;

determining one or more predictor values for one or more predictors associated with the one or more previous behaviors; and

causing, at least in part, a creation of at least one predictive model based, at least in part, on the one or more predictor values,

wherein the at least one predictive model is used to predict one or more potential behaviors by the at least one user while traveling the at least one calculated route, at least one other route, or a combination thereof.

19. A non-transitory computer-readable storage medium of claim 18, wherein the one or more predictor values include, at least in part, personalized data for the at least one user, the method further comprising:

causing, at least in part, a personalization of the at least one predictive model to the at least one user based, at least in part, on the personalized data.

20. A non-transitory computer-readable storage medium of claim 19, further comprising:

determining weighting information for the one or more predictors based, at least in part, on the personalized data,

wherein the personalization of the at least one predictive model is further based, at least in part, on the weighting information.

21-46. -46. (canceled)