Abstract: A method and apparatus for predicting traffic on a transportation network where real time data points are missing. In one embodiment, the missing data is estimated using a calibration model comprised of historical data that can be periodically updated, from select links constituting a relationship vector. The missing data can be estimated off-line whereafter it can be used to predict traffic for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network. The invention further discloses a method for in-vehicle navigation; and a method for traffic prediction for a single lane.
Type:
Grant
Filed:
May 5, 2014
Date of Patent:
March 21, 2017
Assignee:
TomTom Global Assets B.V.
Inventors:
Laura Wynter, Wanli Min, Benjamin G. Morris
Abstract: A method and apparatus for predicting traffic on a transportation network where real time data points are missing. In one embodiment, the missing data is estimated using a calibration model comprised of historical data that can be periodically updated, from select links constituting a relationship vector. The missing data can be estimated off-line whereafter it can be used to predict traffic for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network. The invention further discloses a method for in-vehicle navigation; and a method for traffic prediction for a single lane.
Type:
Grant
Filed:
November 16, 2009
Date of Patent:
June 17, 2014
Assignee:
TomTom Global Assets B.V.
Inventors:
Laura Wynter, Wanli Min, Benjamin G. Morris