## 1 Introduction  
Connected and autonomous vehicles and cooperative intelligent transportation systems (C-ITSs) are new technologies that will impact traffic control and management. Cooperative intelligent transportation systems (C-ITSs) are based on the sharing of information between drivers and road management. Connected vehicles (CAs) will be able to share speeds and positions among many other various useful data. Vehicle speeds and positions can be used to better manage traffic signals in real time. Connected vehicles can become an important part of new C-ITSs. Some of these ideas have been explored in some European-funded projects such as SAFESPOT [1], EuroFOT [2] and DRIVE C2X [3]. One of the main tasks that road managers must deal with in traffic control is the management of signalized intersections. Control operations, unfortunately, very often are not adjusted in real time and sometimes are implemented with out-of-date fixed-time traffic signal settings. Traffic congestion, which can also be caused by traffic signals, is a serious problem in cities and also a great cause of air pollution. For this reason, efforts to solve congestion problems have been centered on attempts to shift demand on transit systems [4] and on better road traffic control, by adopting tools such as traffic simulation [5; 6; 7; 8; 9; 10] dynamic network loading equilibrium and dynamic models [11; 12; 13; 14] and the study of and attempt to affect user route choice [15; 16; 17; 18; 19]. Many large-scale deployments of systems based on floating car data (FCD) are already showing the use of mobile phones [20; 21; 22; 23] and wireless communications [24; 25] combined with global navigation satellite system (GNSS) technologies. Cooperative systems based on smartphones are spontaneously spreading in ordinary use (BlaBla Car, Uber, etc.). Cooperative systems based on smartphones have also been proposed for pedestrians and bicycles [26]. Smartphones (and connected vehicles) can obtain location and speed information from GNSS systems such as Galileo, GPS and Glonass. GPS embedded in smartphones produces an economic method to obtain vehicular travel time [27] and to evaluate traffic scenarios [28; 29]. Smartphones also allow the estimation of traffic safety parameters [30] and path choice [31]. Mobile devices have also been used to assess safety and risks by insurance companies [32] and for traffic safety [33; 34] and fuel consumption estimation [35]. All these concepts have been applied also in the management of signalized intersections: GNSS data coming from private vehicles were useful in the evaluation of adaptive traffic signal systems in [36], and smartphones were useful to gather information on traffic signal timings [37; 38; 39]; the use of FCD in C-ITSs is discussed in [40], where traffic signal regulation from FCD is achieved. The term \"floating car data adaptive traffic lights\" (FCDATL) or \"floating car data adaptive traffic signals\" (FCDATS) was introduced in [41]. The specific use of FCD data coming from smartphones to regulate adaptive traffic signals in real time was also investigated in [42; 43; 44; 45]. In some cases, the adaptive traffic signal system is used in combination with driver assistance, so that both driver behavior and traffic signals are adjusted in real time. Projects such as Colombo [43] have been presented in which the general potential of vehicle communication to other vehicles or infrastructures (V2X) for traffic management is investigated. Some works have also been presented on traffic signal regulation presenting algorithms without a clear definition of the underlying communication and control systems. Among them, [47; 48; 49] propose the use of game theory in traffic signal regulation, implicitly assuming that information on all vehicle positions is available. The scientometric-based review presented in this paper shows that there are few papers presented on actuated traffic signal coordination.