## 2 Related Work
A study used Global Positioning System (GPS) technology to discover accidents by monitoring the speeds of vehicles to discover accidents and share this information with the accident control center, along with the location of the accident [16]. Another study made use of accelerometers for vehicles and GPS sensors in order to monitor the security and safety of commuters, and share the accident information with their relatives [32]. Ref. [17] employed smart devices in accident detection by implementing a special sensor for accident detection. Similarly, work described in [15] used the concept of dynamic time for accident detection through sensitive sensors in vehicles. A built-in circuit based on a micro-controller and sensors for the process of detecting the accident and then sending a signal about it has also been developed [33]. Another study [34] presented an accident control system that sends a text message after recognizing the occurrence of an accident through sensors that capture vibrations. The text message is sent to emergency services and contains the GPS location of the accident. Similar work can be found in [35]. Furthermore, other technologies, such as IoT [36], have been investigated for this purpose. Monitoring and managing traffic using IoT has been proposed in the literature [37]. These studies generally rely on driver data to support decision-making in relation to traffic lights. It has been argued that as IoT connects physical things to the internet to build smart systems such as intelligent transportation systems (ITSs), communication among vehicles will create a new age of services in the land, sea, and air [38]. In fact, in the Internet of Vehicles (IoV), vehicles will act as sensing points, which bring more services, safety, and efficiency for transportation systems [39]. Moreover, the IoV can be used to provide sensors for environmental conditions, which can meet the needs of smart cities. It has also been suggested that integrating ITS with IoT can achieve the goal of smart transportation [40]. The authors of that study also suggested the use of fog computing. In another such study [41], an increase in location-based service (LBS) applications, especially in smart cities with the IoT, has been observed. Based on this observation, the authors proposed a new framework for merging many IoT services in the transportation sector to monitor public transport. At the same time, researchers have also highlighted the need for a new level of security and privacy to protect the data of users in such a merger of technologies [42]. Preserving privacy is a vital issue in smart systems, which has to be considered because it is the main threat faced by the IoT and its applications [43; 44]. Many studies have classified attackers that pose threats to the privacy of users' data and have discussed their skills [45]. Nevertheless, surveys on methods and techniques for preserving privacy have also been discussed [46; 47]. A complete overview for the IoT and its structure, layers, phases, applications, and future trends can be found in [48; 49]. Using big data to address the congestion issue has also been proposed [50]. Ref. [51] discussed the challenges and drivers of using machine learning algorithms with big data in the transportation field, using Hadoop and Map-Reduce to manage these data and to perform data analytics. The result can be useful in many applications such as parking, vehicle sharing, automatic vehicle positioning, etc. Using fog computing to address these challenges has also been suggested [52]. Ref. [53] discusses the challenges of using fog computing in ITSs. On the other hand, smart transportation has extended beyond traffic management and is addressing vehicular transportation in general. Studies have investigated harvesting energy using roads, through solar energy and vibrational energy generated by vehicles. This energy is utilized for lighting either roads and traffic lights, and can be used to supplement the electricity grid [54].