Data Governance in Traffic Data: Anomaly Detection with Generalized Additive Models
Zuzana Purkrábková, Martin Langr, Pavel Hrubeš, Marek Brabec · Neural Network World · 2024
The primary objective of the presented research is to enhance an existing data quality control application by integrating advanced anomaly detection mechanisms based on generalized additive models. This approach targets time- series traffic data, where traditional methods may fall short in identifying complex, non-linear patterns of anomalies. In collaboration with Simplity s.r.o., we are extending their current data quality assessment tool to incorporate generalized additive models, providing a more robust and dynamic solution for monitoring and ensuring the reliability of traffic datasets. The integration of these models aims to improve the accuracy of anomaly detection, leading to more effective data management in transport systems and contributing to higher standards of data quality in the field of traffic informatics.