This paper presents experimental findings from a field study conducted in Yokohama, Japan, to investigate the existence of a macroscopic fundamental diagram (MFD) linking space-mean flow, density, and speed in urban areas. The study used a combination of fixed detectors and floating vehicle probes, specifically GPS-equipped taxis, to gather data. The results show that when individual scatter-plots of speed vs. density from fixed detectors are aggregated, the scatter nearly disappears, forming a smoothly declining curve. This suggests the presence of an MFD for the complete network, although the fixed detectors only measure conditions in their proximity. The analysis was further enriched with data from GPS-equipped taxis, which covered the entire network. The estimated space-mean speeds and densities at different times-of-day were found to lie close to a smoothly declining curve, with deviations explained by experimental error. Additionally, a fixed relation between space-mean flows and trip completion rates was observed, indicating that trip completion rates can be dynamically predicted from observable data. The findings suggest that large urban networks can behave predictably and independently of their origin-destination tables, providing reliable tools for traffic management and planning.This paper presents experimental findings from a field study conducted in Yokohama, Japan, to investigate the existence of a macroscopic fundamental diagram (MFD) linking space-mean flow, density, and speed in urban areas. The study used a combination of fixed detectors and floating vehicle probes, specifically GPS-equipped taxis, to gather data. The results show that when individual scatter-plots of speed vs. density from fixed detectors are aggregated, the scatter nearly disappears, forming a smoothly declining curve. This suggests the presence of an MFD for the complete network, although the fixed detectors only measure conditions in their proximity. The analysis was further enriched with data from GPS-equipped taxis, which covered the entire network. The estimated space-mean speeds and densities at different times-of-day were found to lie close to a smoothly declining curve, with deviations explained by experimental error. Additionally, a fixed relation between space-mean flows and trip completion rates was observed, indicating that trip completion rates can be dynamically predicted from observable data. The findings suggest that large urban networks can behave predictably and independently of their origin-destination tables, providing reliable tools for traffic management and planning.