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Vehicular Ad Hoc Networks (VANETs) are mobile networks whose main objective is to provide communication between vehicles (cars, trucks, buses, etc.) and are part of Intelligent Transportation Systems (ITS) (Alves Jr & Wille, 2015). The goal of ITS is to improve the user experience (drivers, passengers, and pedestrians) in traffic by providing safety, conscious and efficient use of resources, and entertainment. It provides real-time information such as adverse road conditions, weather, congestion, and local tourist information. This information helps to plan the route, reducing environmental pollution, improving vehicle performance and contributing to the users' well-being (Alam & Ferreira, 2016) (Li, Zhen, Sun, Zhang, & Hu, 2016), (Alrawi, 2017), (Hong Zhang and Xinxin Lu, 2020).
The nodes of such networks communicate with one another by means of radio-frequency signals. As radio signals have a limited power, each node can directly communicate with those vehicles within transmission coverage. However, frequently there is the necessity of transmitting information to some out-of-range vehicles. In order to accomplish that, the vehicles must cooperate with each other, acting as routers (finding routes and passing information from origin to destination). The sending/receiving of information only occurs when the origin vehicle has a route to the destination vehicle (Almohammedi, 2016). As vehicular networks are self-organizing and configuring networks, with decentralized control, their performance is utterly dependent on the existence of routes and the time these routes stay established (while a route stays established, both the origin and the destination are able to send and receive data) (Alves Jr & Wille, 2016).
In VANETs, the main communication challenges are related to connectivity problems between vehicles due to short route duration times (Tong, L.; Xia, Z.; Shi, S. & Gu, X., 2018). Knowing and evaluating these times and the factors which most impact them could help developing more adequate protocols and applications in order to optimize network connectivity, as well as to contribute to a more efficient vehicular communication infrastructure. In these networks, the main factors that can influence the route duration times and network connectivity are: speed, density, travel orientation and transmission range of the vehicles (Alves Jr & Wille, 2016).
Most of the analyses of route duration times and network connectivity are carried out through numerical studies (Raw, Kumar Soni, Singh, & Kaiwartya, 2014), (Ajeer, Neelakantan, & Babu, 2011), (Nazar & Alsabbagh, 2016). Such studies take into consideration the values of speed, density and movement orientation of the vehicles (the most influential factors), but do not consider the infrastructural conditions (roads, squares, intersections and traffic lights) and also the vehicular flow (traffic jams). Because of this, some results may not faithfully represent the reality of vehicular mobility.