Data Quality in Traffic Management: Framework and Real-World Impacts

Effective traffic management relies on the availability of high-quality traffic data to support real-time decision-making for optimizing traffic flow, enhancing safety, and reducing environmental impacts. This study aims to address the lack of integrated and operational approaches for traffic data quality management by proposing a scalable and adaptable framework for the systematic assessment and enhancement of traffic data. The framework consists of four interconnected layers, including data ingestion, data quality assessment, data imputation and correction, and a real-time alerting mechanism. Its applicability is demonstrated through a real-world case study on traffic signal control plan selection, using sensitivity and simulation-based analyses in SUMO. The results indicate that degraded data quality, particularly due to missing or invalid records, can significantly affect system behavior, leading to suboptimal decisions and reduced traffic performance. These findings highlight the importance of continuous and systematic data quality monitoring as a critical component for reliable and efficient traffic management systems.