Dynamic prediction of traffic congestion by tracing feature-space trajectory of sparse floating-car data

Abstract

A traffic situation is predicted based on the correlation in the traffic situation between road sections. A base vector generation unit generates the base vectors constituting a feature space representing the correlation between a plurality of links by making a principal component analysis for the necessary time in the past recorded in a necessary time database. A projection point trajectory generation unit records a projection point trajectory of projecting the necessary time in the past recorded in the necessary time database to the feature space in a projection point database. A feature space projection unit projects the necessary time at present to the feature space, and a neighboring projection point retrieval unit retrieves a past projection point in the neighborhood of the concerned projection point from the projection point database, and a projection point trajectory trace unit traces the trajectory of past projection points starting from the retrieved neighboring projection point for a prediction target time width, and an inverse projection unit inversely projects the end point of the concerned trajectory to calculate the predicted value of the necessary time.