Recently, the author proposed a new approach how to take advantage of common floating car data in context of urban traffic monitoring (cf. Neumann, 2009: Efficient queue length detection at traffic signals using probe vehicle data and data fusion, 16th ITS World Congress, Stockholm). Treading an innovative path concerning the processing of observed vehicle positions, it provides a promising method for estimating queue lengths at signalized intersections. Moreover, the conceptual ideas of the approach are not even limited to this very special case as has been discussed shortly in former publications. In principle, unsignalized intersections and/or other traffic state variables can be considered as well. Previous presentations and articles, however, always focused on the algorithmic aspects of the new technique and on the quality of results. In contrast to that, this contribution takes up the practitioner’s perspective and examines the wide range of possible applications of current and future versions of the proposed ideas. After a short review about the basic concepts and after summarizing the corresponding main results from former studies, the paper discusses capabilities and limitations of the new approach and shows how these are influenced by several constraints such as the available number of floating cars and/or suboptimal model parameters. Based on that, typical applications in traffic planning and control are identified which might benefit from enhanced versions of the described method. Conceivable fields are traffic signal control, traffic assignment and simulation, quality management or dynamic routing, for instance. Moreover, the new approach theoretically allows for the incorporation of arbitrary supporting traffic information. That is, additional detector data as well as the results of other complex traffic monitoring systems can be integrated efficiently into the framework of the proposed method to improve the overall quality of urban traffic state estimation. Particularly, existing systems have not necessarily to be replaced but can be used further on in a bigger context. Finally, future cooperative systems (C2X) will probably provide the facility of enabling also private cars for traffic monitoring to a large extent in sense of continuously sampling their positions in the road network, for instance. Hence, if implemented in a suitable way, such data were not only capable of being integrated into the described algorithms as supporting information (using a standardized data fusion interface) but would comprehensively complement the new method’s basic data source itself. In doing so, the most crucial drawback of common floating car systems, i.e. low penetration rates, were simply overcome. Notably, possibly uprising privacy concerns in politics and society are easily rejected at the same time as the proposed method gets along completely without any kind of vehicle identification or vehicle tracking. That is, a major hurdle when talking about observing individual cars in traffic does not apply in this case.