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Abstract The conversion from single-entity level characteristics of traffic flow to comparable system-level characteristics shaped a new era for traffic monitoring and control. Since then landmark studies explored network-level traffic flow relationships across entire urban networks or regions and cities, mainly based on simulation data but also with empirical data. Although, the ability to observe and monitor the traffic state of the system on a network-wide level depends on the availability of existing traffic surveillance systems, adequately deployed such as to cover a complete network. To overcome this deficit, we propose a method to estimate a network’s Macroscopic Fundamental Diagrams (MFD) using traffic flow mechanics at the microscopic level and exploiting class-type traffic information that can be obtained from online traffic maps. This valuable information depicted on maps is extracted based on image processing techniques, able to simultaneously perform discretization of the urban space-and the road network therein- in seamless pixels and further capture the color-coded traffic information in a suitable data structure valuable for meta-analysis. Then, the fundamental traffic flow mechanics are used for connecting the captured pixels properties with macroscopic traffic phenomena, especially with the well-defined (MFDs). The validity of the method is tested by comparing the estimated MFDs to ground-truth MFD obtained using empirical data from loop detectors. The results are providing valuable evidence on the operational characteristics of large urban areas, while at a meta-analysis stage it was able to capture spatio-temporal phenomena of urban mobility, like concentration, hysteresis and homogeneity. Since online traffic maps provide almost global coverage the proposed method is practically feasible and offers a novel approach for monitoring large-scale traffic systems.

transportation research procedia

Linking the microscopic traffic flow mechanics with the macroscopic phenomena by exploiting class-type traffic information retrieved from online traffic maps