DMP-FINAL: Predicting Urban Traffic Congestion Levels using Multi-Layer Perceptrons on SDCC SCOOT Data

The Final Data Management Plan (DMP) and machine-actionable DMP (maDMP) represent the comprehensive documentation of the 'Predicting Urban Traffic Congestion' project’s data lifecycle. Following the RDA DMP Common Standard 1.2, these deliverables provide a formal, interoperable record of the dataset provenance, metadata standards, and preservation strategies employed. The plan explicitly links the SDCC SCOOT traffic data with the trained MLP model artifacts and GitHub source code via persistent identifiers (DOIs). It documents licensing (CC BY 4.0) and institutional hosting on the TU Wien DBRepo and Research Data repositories, ensuring that all research outputs—from raw sensor data to trained model weights—remain findable, accessible, interoperable, and reusable (FAIR) for future urban mobility research.