Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"doi:10.60507/FK2/DRYP80bonndata2026-01-082Gounoue, Steve, 2026, "Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"", https://doi.org/10.60507/FK2/DRYP80, bonndata, V2Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"doi:10.60507/FK2/DRYP80Gounoue, StevebonndataGounoue, SteveGounoue Guiffo, Steve2026-01-06Computer and Information ScienceEngineeringIn 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.<a href="https://mit-license.org/">MIT</a>SCANNER: A Spatio-temporal Correlation and Neighborhood-based Feature Enrichment for Traffic Prediction: https://github.com/D-Stiv/SCANNERSteve Gounoue, Ran Yu, and Elena Demidova. 2026. SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction. ACM Transactions on Spatial Algorithms and SystemsSteve Gounoue, Ran Yu, and Elena Demidova. 2026. SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction. ACM Transactions on Spatial Algorithms and SystemsSteve Gounoue, Ran Yu, and Elena Demidova. 2023. SCANNER: A Spatio-temporal Correlation and Neighborhood-based Feature Enrichment for Traffic Prediction. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23). Association for Computing Machinery, New York, NY, USA, Article 103, 1–4.10.1145/3589132.3625653Steve Gounoue, Ran Yu, and Elena Demidova. 2023. SCANNER: A Spatio-temporal Correlation and Neighborhood-based Feature Enrichment for Traffic Prediction. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23). Association for Computing Machinery, New York, NY, USA, Article 103, 1–4..gitignoretext/plainconfig.pytext/x-pythoncorrelations.pytext/x-pythonLICENSEtext/plain; charset=US-ASCIIloader.pytext/x-pythonmain.pytext/x-pythonmetrics.pytext/x-pythonmodels.pytext/x-pythonREADME.mdtext/markdownrequirements.txttext/plainstnorm.pytext/x-pythontrainer.pytext/x-pythonutil.pytext/x-python