TRAFFIC PREDICTION

A computer implemented method for training a learning model for traffic prediction at respective localities by means of a learning system includes a convolution engine and an encoder-decoder. The method involves constructing a graph representation of the localities based on a spatial relation betwee...

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Bibliographic Details
Main Authors HELLINCKX, Peter, BOGAERTS, Toon, BOSMANS, Stig, CASTEELS, Wim
Format Patent
LanguageEnglish
Published 15.02.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
SIGNALLING
TRAFFIC CONTROL SYSTEMS
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Summary:A computer implemented method for training a learning model for traffic prediction at respective localities by means of a learning system includes a convolution engine and an encoder-decoder. The method involves constructing a graph representation of the localities based on a spatial relation between the respective localities; populating the constructed graph with traffic data characterizing the traffic in the respective localities at respective time periods; convolving, by the convolution engine, for a respective locality and for a respective time period, the traffic data in the respective locality with the traffic data in its neighboring localities, thereby obtaining relation-based traffic representation; processing, by the encoder-decoder, for a respective locality and for a respective time period, the relation-based traffic representation, thereby obtaining a gradient information; and updating, for a respective locality, the learning model with the obtained gradient information, thereby training the learning model.
Bibliography:Application Number: US202118267624