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License: MIT

1 to 10 of 412 Results

Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Jan 8, 2026 Gounoue, Steve, 2026, "Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"", https://doi.org/10.60507/FK2/DRYP80, bonndata, V1 In 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.
config.py Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 4.9 KB - MD5: 3ed7e52601c8b8dc8593dfd451d74117
correlations.py Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 4.0 KB - MD5: 09b1b8e27396fcba6b53000a69e2c7f2
LICENSE Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Plain Text - 1.0 KB - MD5: 6ffa33bfbd01598cc0213f17c6ca3509
loader.py Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 8.3 KB - MD5: df39204d1cbe79a9c7ae27cb4feb3aca
main.py Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 2.7 KB - MD5: 27126d3edf4389c34fe97aa1d0f491f7
metrics.py Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 3.2 KB - MD5: 0ce397441f28765641144980b23fe580
models.py Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Python Source Code - 15.4 KB - MD5: fcd653ba75d56e74409a4b733556f787
README.md Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Markdown Text - 3.4 KB - MD5: 1e85be2c22714053b65dfbc35301ee0b
requirements.txt Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction" Plain Text - 207 B - MD5: f77812664de342d6e77bbabc25067643

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

Jan 8, 2026

Gounoue, Steve, 2026, "Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"", https://doi.org/10.60507/FK2/DRYP80, bonndata, V1

In 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.

config.py

Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Python Source Code - 4.9 KB -

correlations.py

Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Python Source Code - 4.0 KB -

LICENSE

Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Plain Text - 1.0 KB -

loader.py

Jan 8, 2026 - Implementation of the paper "SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction"

Python Source Code - 8.3 KB -