## 1 Introduction  
Thousands of people lose their lives annually due to road accidents, which also cause many disabilities and injuries, and contribute to other catastrophes through environment pollution due to traffic congestion [1]. Economic and health aspects are most significantly affected by traffic congestion due to delays in the execution of daily tasks, causing a huge waste of time, effort, and focus, as well as other additional costs [2]. Therefore, traffic congestion has become a fundamental problem and a major challenge for most countries of the world. Advances in digital technology in recent years have an effective role to play in solving this problem. As an example, the Internet of Things (IoT) has transformed everything around us into smart things [3], with a unique identity and the ability to sense and collect information from the surrounding environment, and share this information using radio-frequency identification (RFID) and wireless sensor networks (WSNs) [4]. RFID identifies an object (for example, a vehicle) using a unique identifier that enables the surrounding environment to identify it, communicate with it, and track its location, whereas WSNs help the vehicle to obtain information such as images, sound, temperature, pressure, pollution level, and so on, from the surrounding environment. The analysis of these data can reveal a lot of useful information that can help to improve traffic congestion. The field of data science and its related technologies (such as machine learning, text mining, data mining, deep learning, and so on) can be leveraged for this purpose. However, the amount of data generated by these devices is huge and require high computing, storage, and energy capacity to process and analyze it [5,6]. Unfortunately, IoT devices have very limited computing resources at their disposal. Therefore, IoT devices usually resort to cooperating with the cloud for data storage and processing. However, due to inherent network latency problems, applications that demand real-time responses, such as healthcare, traffic monitoring, and so on, cannot be realized in this approach. Nevertheless, fog computing allows data from these sources to be collected by an initial station and enables users to apply policies to these data to filter, analyze, and discover any emergency events to deal with them immediately. Subsequently, data can be sent to the cloud for permanent storage, so that specific analyses can be conducted in the future to provide knowledge over a long period of time to facilitate decision-making. Furthermore, advances in data science, specifically through applications in other fields, has led to the emergence of many algorithms that can perform a variety of specialized tasks. In fact, these technologies are being used aggressively to provide smart and dynamic solutions in many areas of research. Unfortunately, this amalgam of technologies is yet to be investigated, applied, and tested in relation to solving the problem of traffic congestion. In this paper, we propose a comprehensive and efficient framework for the traffic congestion problem, based on the integration of these modern technologies. The main contributions of this paper include:  
1. A review of the literature related to traffic congestion. 2. A comprehensive framework for a reliable, flexible, and efficient solution for traffic congestion problems. 3. An algorithm for traffic lights for the efficient management of congestion at intersections and the evaluation of its efficiency. 4. An algorithm for the classification of tweets and the detection of congestion in a particular area and the evaluation of its accuracy. 5. An accuracy evaluation of an image-processing algorithm for congestion detection from images captured by drones. The rest of this paper is organized as follows.