Auto-calibration for road traffic prediction

Patent metadata

Field	Value
Publication number	US-9412267-B2
Application number	US-201414518212-A
Country	US
Kind code	B2
Filing date	Oct 20, 2014
Priority date	Mar 19, 2014
Publication date	Aug 9, 2016
Grant date	Aug 9, 2016

Abstract

A method for auto-calibrating parameters in traffic prediction. The method includes determining a first subnet of traffic links that is associated with a plurality of traffic links in a traffic network. The method includes determining a second subnet of traffic links that is associated with the first subnet of traffic links and has a first traffic predicting accuracy value. The method includes generating a set of optimized traffic predicting parameters associated with the second subnet of traffic links, and applying the set of optimized traffic parameters onto a third subnet of traffic links. The method includes determining the set of optimized traffic predicting parameters used to calculate prediction results having a second traffic predicting accuracy value, and applying said set of optimized traffic predicting parameters to subnets associated with the traffic network. Further, the first traffic predicting accuracy value is lower than the second traffic predicting accuracy value.

First claim

What is claimed is:

1. A computer-implemented method for auto-calibrating parameters in traffic prediction, the method comprising:
determining, by one or more computer processors, a first subnet of traffic links having an association with a plurality of traffic links in a traffic network;
determining, by one or more computer processors, a second subnet of traffic links that is associated with the first subnet of traffic links, wherein the second subnet of traffic links has a first traffic predicting accuracy value;
generating, by one or more computer processors, a set of optimized traffic predicting parameters associated with the second subnet of traffic links;
storing, by one or more processors, the set of optimized traffic predicting parameters associated with the second subnet of traffic links in a database;
retrieving, by one or more processors, the stored set of optimized traffic predicting parameters associated with the second subnet of traffic from the database;
applying, by one or more computer processors, the set of optimized traffic predicting parameters onto a third subnet of traffic links;
determining, by one or more computer processors, the set of optimized traffic predicting parameters used to calculate prediction results having a second traffic predicting accuracy value;
applying, by one or more computer processors, the set of optimized traffic predicting parameters used to calculated prediction results associated with the second traffic predicting accuracy to subnets associated with the traffic network;
wherein the first traffic predicting accuracy value is lower than the second traffic predicting accuracy value; and
displaying, by one or more processors, a representation of a traffic predicting accuracy for subnets associated with the traffic network.

2. The method of claim 1 , wherein the first traffic predicting accuracy value or second traffic predicting accuracy value is proportional to the difference between:
a predicted value obtained using parameter data from all links in the traffic network; and
an observed value, obtained using a real-time vehicular traffic information feed associated with all links in the traffic network.

3. The method of claim 1 , wherein the set of traffic predicting parameters includes:
an alpha parameter that reflects a weight applied to a recent past versus a more distant past;
a beta parameter that reflects a number of steps, or hops, between traffic links;
a gamma parameter that reflects a number of weeks of historical data used for a mean calculation;
a zeta parameter that reflects a number of weeks of historical data used for an estimate calculation;
a delta parameter that reflects a number of data points of past data; and
a theta parameter that reflects a quality of data input from a real-time vehicular traffic information feed.

4. The method of claim 3 , wherein the mean calculation comprises a calculation of a historical mean value using the gamma parameter.

5. The method of claim 3 , wherein the estimate calculation comprises a calculation of a traffic volume of each link in the second subnet, using the zeta parameter.

6. The method of claim 3 , wherein the step of generating, by the one or more computer processors, the set of optimized traffic predicting parameters comprises:
selecting the second subnet of traffic links;
increasing the beta parameter one hop;
increasing the alpha parameter, the gamma parameter, the zeta parameter, and the delta parameter by one step; and
executing at least one of the mean calculation, the estimate calculation, and a traffic predicting accuracy calculation, using one or more of the increased alpha, beta, gamma, zeta, and delta parameters.

Assignees

• IBM

Inventors

• Joseph Jojo
• Nair Raghunath E

Classifications

• G08G1/0112 Primary

  from the vehicle, e.g. floating car data [FCD] · CPC title

• G08G1/0129

  for creating historical data or processing based on historical data · CPC title

• G08G1/0116

  from roadside infrastructure, e.g. beacons · CPC title

• G08G1/0133

  for classifying traffic situation · CPC title