Method for predicting future travel time using geospatial inference

• US 9,122,987 B2
• Filed: 01/17/2013
• Issued: 09/01/2015
• Est. Priority Date: 01/17/2013
• Status: Expired due to Fees

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Jones, Michael, Nikovski, Daniel, Geng, Yanfeng
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Figueroa, Kevin W
• Application Number:
  US13/743,996
• Publication Number:
  US 20140201121A1
• Time in Patent Office: 957 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/096827 where the route is computed onboard
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data

Abstract

Future travel times of a target vehicle traveling on a route from a starting point to a destination are predicted by first acquiring, by a probe vehicle, real-time probe data to alternative links from the starting point to the destination. Then, the future travel time for each link is predicted using a set of regression functions.

Claims

1. A method for predicting future travel times of a target vehicle traveling on a route from a starting point to a destination, comprising the steps of:

acquiring real-time probe data by probe vehicles traveling on a set of one or more alternative links, which are substantially parallel to the route traveled by the target vehicle, from the starting point to the destination;

estimating a current travel time for each alternative link using the real-time probe data; and

predicting the future travel time of the target vehicle traveling on the route based on the current travel times for the set of one or more alternative links using a set of regression functions, wherein each regression function is

2. The method of claim 1, wherein G is a Gaussian function.

3. The method of claim 1, wherein the set of regression functions is learned off-line using historical probe data.

4. The method of claim 1, further comprising:

outputting the future travel time to an operator of the target vehicle.

5. A system for predicting future travel times of a target vehicle traveling on a route from a starting point to a destination, comprising:

a probe vehicle configured to acquire real-time probe data for a set of one or more alternative links, which are substantially parallel to the route traveled by the target vehicle, from the starting point to the destination; and

a processor in the target car configured to predict the future travel time of the target vehicle traveling on the route based on the current travel times for the set of one or more alternative links using a set of regression functions, wherein each regression function is