Method for Predicting Travel Destinations Based on Historical Data

• US 20150134244A1
• Filed: 11/12/2013
• Published: 05/14/2015
• Est. Priority Date: 11/12/2013
• Status: Abandoned Application

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Hershey, John R., Li, Lingbo, Li, William
• Application Number:
  US14/077,689
• Publication Number:
  US 20150134244A1
• Time in Patent Office: Days
• US Class Current: 701/489
• CPC Class Codes: G01C 21/3617 using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement

Abstract

The embodiments of the invention provide a method in a navigation system, for predicting travel destinations according to a history of destinations. A model used for the prediction incorporates a database of destinations, which can include favorite, i.e., most probable, destinations for a user. The model also uses a context that can include features such as a current time of day, day of week, current location, current direction, past location, weather, and so on. The model infers the destination and destination categories even when the destination is not known precisely. Specifically, a method predicts destinations during travel, based on feature vectors representing current states of the travel, probabilities of destinations and categories of the destinations using a predictive model representing previous states of the travel. A subset of the destinations and categories of the destinations with highest probabilities are output for user selection.

Claims

1. A method for predicting destinations during travel comprising steps:

inferring, based on previous states of the travel, probabilities of having traveled to destinations and destination categories in the past;

predicting, based on feature vectors representing current states of the travel, probabilities of categories of the destinations using a predictive model based on previous states of the travel and the destinations and destination categories, wherein the feature vectors include vehicle navigation data, vehicle system bus data, weather data, and derived data;

regularizing parameters of the predictive model;

transforming the feature vectors to a lower-dimensional subspace; and

outputting a subset of the categories with highest probabilities for user selection, wherein the steps are performed in a processor.

3. The method of claim 1, wherein the predictive model is based on an N-gram.

4. The method of claim 1, wherein the model is a probability+unit (probit) regression model, where dependent variable can only take two values.

6. The method of claim 1, wherein the predicting uses a probabilistic model.

7. The method of claim 1, predicting the destinations using a multinomial distribution.

8. The method of claim 1, wherein categories include hierarchies of genres, names, and destinations.

9. The method of claim 1, wherein the predicting uses a combination of databases of destinations, and a history of locations.

2. (canceled)

5. (canceled)