## 2 Literature Review
attempted to identify the relationship between vehicle speeds in uninterrupted and interrupted traffic stream conditions using the method of characteristics [47]. In a study conducted in 2021, [PERSON] and [PERSON] performed a calibration of the most commonly used volume-delay models, including BPR, Conical, Akcelik and modified Davison model, for continuous and interrupted traffic stream conditions in different road categories in the urban area of Thessaloniki in Greece [48]. In recent years, a growing number of researchers have used unmanned aerial vehicles (drones) to capture the aerial footage of traffic flows, based on which, it is possible to extract empirical values of relevant traffic flow parameters [49; 50; 51; 52; 53; 54; 55]. This innovative approach facilitates the analysis, comparison and assessment of prevailing traffic stream characteristics across various road network segments. One of the main advantages of using unmanned aerial vehicles for traffic data collection stems from the fact that they allow data collection without significantly disrupting normal traffic flow. Therefore, by using drones, it is possible to ensure that the collected data remain representative of real-world conditions without introducing bias into samples, which are used to develop the \"speed-density\" and \"flow-density\" regression models. Furthermore, drones equipped with high-resolution cameras and advanced sensors can capture much finer details of complex traffic flow spatiotemporal characteristics, and aerial video footage recorded by drones can be used for numerous research purposes. With the further development and integration of various algorithms and software based on artificial intelligence and machine learning, aerial footage can be automatically processed and used to detect and track the movement of individual vehicles in a traffic stream, identify complex traffic flow patterns, extract the trajectory of vehicles, detect various traffic flow anomalies, predict potential traffic flow congestions and vehicle collisions. The ability to utilize these computational tools to process aerial video data far surpasses the limited possibilities of traditional traffic data collection methods. In addition to that, aerial video footage can be stored and used again at any time to conduct additional research. Regardless of the fact that the implementation of commercially available unmanned aerial vehicles is still limited by their relatively short battery life, there is no doubt that, in the future years, they will become fully accepted as one of the most advanced, accurate and reliable techniques for the collection of traffic data. Quite a number of researchers have already implemented unmanned aerial vehicles for the purpose of capturing aerial video footage of traffic flows. For instance, [PERSON] and his team explored the potential of utilizing unmanned aerial vehicles for simultaneous traffic state estimation and incident detection on multiple road segments [56]. In a 2021 study, [PERSON], [PERSON], [PERSON] and their colleagues proposed specific functional forms of fundamental traffic flow diagrams for describing the characteristics of heterogeneous and undisciplined traffic streams [57]. Specific functional forms of fundamental diagrams were developed based on empirical traffic data gathered from aerial footage captured by drones over representative road segments in the city of Karachi in Pakistan. Recently, an increasing number of researchers suggested that the \"speed-density\" relationship can be more accurately described by compound forms of logarithmic, exponential, potential, logistic and sigmoid S-shaped multi-parameter functions [58; 59; 60; 61; 62; 63]. Besides this, it should also be considered that various other types of mathematical models used in other fields, such as Morgan-Mercer-Fledin growth model, could be modified for the purpose of describing the relation between traffic flow speed and density [64].