Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

10.60507/FK2/DRYP80Gounoue, SteveSteveGounoue0009-0003-4580-3173Data Science and Intelligent Systems Group (DSIS), University of BonnImplementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"bonndata2026Computer and Information ScienceEngineeringGounoue, SteveSteveGounoueData Science and Intelligent Systems Group (DSIS), University of Bonn2026-01-062026-01-2610.1145/3589132.3625653344310634213497941969823290037061195532421309761366text/plaintext/plain; charset=US-ASCIItext/markdowntext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/x-pythontext/plaintext/x-pythontext/x-pythontext/x-python2.0MITIn this repository, you can find the code to train and evaluate SCANNER+, a novel neighborhood-based self-enrichment approach for traffic speed prediction. SCANNER+ learns effective node representations in dynamic road traffic settings. This work extends SCANNER, which utilizes correlation-based pattern detection and a self-enrichment mechanism.