## 1 Introduction  
The Intelligent Transportation Systems (ITS) research field has attracted careful consideration of the research and industrial communities in the last years, also because of its enormous potential in terms of economic and societal impact. It is widely recognized that, through the usage of vehicular communications, many novel valuable services can be enabled, spanning from enhanced safety of road users and provisioning of advanced applications to drivers/passengers (e.g., best path determination and infotainment), to optimized vehicular traffic flow management in order to efficiently exploit the available road infrastructure. Indeed, it is well accepted that the identification of good and economically sustainable traffic management solutions is becoming one of the top priorities for future Smart Cities [1; 2]. In particular, in dense urban environments, vehicular traffic congestions are a major problem that is documented to generate serious environmental and economic costs, as well as to lead to user frustration [3; 4]. Given the recent advancements and the strong industrial interest in the related technologies, with ever-increasing frequency, ITS solutions for traffic optimization make extensive use of Vehicular Ad-hoc Networks (VANETs), which enable different forms of vehicular communications: between vehicles (V2V), between mobile nodes and fixed infrastructure points (V2I), and a combination of the two (V2X). To successfully apply VANET-related technologies to the field of vehicular traffic monitoring and surveillance in smart cities, however, some relevant and still open technical challenges have to be overcome, such as the low penetration rate of vehicles with wireless communication capabilities or at least of vehicles willing to trustfully serve in a traffic surveillance cooperative service, a scenario that will likely persist in the near future. In addition, even the future envisioned scenario of high concentration of communication-enabled vehicles poses serious technical challenges, e.g., because of wireless medium congestion and consequent performance degradation deriving from high quotas of vehicles locally participating to a cooperative service via their wireless connectivity capabilities. Moreover, to enable ready-to-use and reliable traffic management optimizations, e.g., via advanced traffic light control algorithms that alleviate traffic congestion based on traffic monitoring data and predictions, there is the need of traffic surveillance solutions that are at the same time: (i) easily and cost-effectively deployable and (ii) precise and robust enough to solidly support delicate traffic management tasks. Several interesting solutions for traffic surveillance solutions are available in the recent related literature, but tend to work under the strongly simplifying assumption of complete penetration rate of communication capabilities in the considered vehicle population [5] or, when considering partial penetration rates, they assume that the rate is statically known and that communication-enabled vehicles are uniformly distributed over the road network [6]. These assumptions are far from reality in any case of practical industrial applicability. In addition, the existing traffic surveillance protocols generally collect only a limited range of monitoring data (typically only the density of vehicles over a target area or the number of vehicles travelling on a specific road, sometimes without the capability of distinguishing the fractions moving in the different directions). Moreover, the quality of the collected monitoring data strongly depend on the penetration rate of the proposed solution (both communication capabilities and adoption of the proposed protocol by targeted participants), thus being of limited value as traffic flow indicators to enable traffic management strategies in realistic smart city scenarios.