Data Evaluation for Continuous Monitoring of Urban Travel

Key Performance Indicators (KPIs), such as modal split or vehicle kilometres travelled (VKT), are essential for assessing mobility trends and informing policy decisions. Traditionally, such indicators rely on travel survey data, which, while comprehensive, are costly and time-consuming to collect. In recent years, alternative data sources, such as mobile phone data, floating car data, and sensor-based measurements, have become increasingly available, offering new opportunities for more frequent and spatially detailed mobility analyses. This paper examines various data sources that can be used for KPI calculation, highlighting their strengths and weaknesses. We review existing studies that have successfully combined two or more of these data sources to gain a more comprehensive understanding of travel behavior. The key focus is on methodological challenges of data fusion, including spatial and temporal resolution issues, representativeness, and consistency between datasets. Furthermore, we discuss the role of travel surveys in supporting data integration. While surveys alone cannot provide continuous travel monitoring, they offer valuable microdata on travel behavior, which can help validate and calibrate other data sources. Finally, we outline best practices for combining different mobility data sources to enhance the accuracy and reliability of transport indicators.