Integrating Multiple Data Sources for Enhanced Urban Commuter Traffic Monitoring: A Cordon-Based Data Fusion Approach

Effective urban commuter traffic monitoring is essential for planning sustainable transport systems in metropolitan regions. Traditional methods, relying on labor-intensive on-site surveys, are often limited by small sample sizes and infrequent data collection. This study introduces a novel data fusion framework that integrates multiple data sources, including automated vehicle counts, manual occupancy counts, floating phone data, and public transport passenger counts. By merging these data sources, the framework enhances data accuracy and reliability, allowing for the calculation of detailed origin–destination matrices and the modal split across various cordon cross-sections at a cordon line. Applied in the Austrian Vienna region over two consecutive years, the framework demonstrated its ability to reduce survey costs, improve data reproducibility, and therefore enable timely evaluation of transport policies and infrastructure projects. The findings revealed a correlation between public transport accessibility and modal split. Furthermore, it was observed that longer travel distances were strongly associated with increased public transport usage, particularly along major rail lines. The presented approach offers valuable insights for urban transport planning, which can be used for data-driven subsidizing of public transport based on patronage and residency, optimizing timetables, and planning metro extensions to meet future mobility needs. The framework’s flexible design and reliance on widely available data ensure its transferability to other metropolitan regions, promoting the development of sustainable transportation strategies.