## 1 Introduction  
The rapid increase in the growth of vehicle ownership has bought significant strain to the traffic flow of vehicles in developed and developing countries, which has led to tremendous changes in the daily lives of pedestrians and motorists [1; 2; 3; 4]. However, it is important to determine the solution to traffic congestion through efficient traffic management and the application of intelligent transportation systems (ITS), especially when referring to a short-term forecast traffic flow (i.e., 60 min) [5; 6; 7; 8]. Traffic-flow forecasting helps traffic signal control, volume management, and travel-route planning. In real-life traffic-flow situations, traffic data are significant. Vehicular traffic flow is often defined by non-linear and highly intricate characteristics, making traffic-flow prediction difficult to determine in transportation engineering. The ability to predict traffic congestion is critical to the successful deployment or application of smart transportation subsystems which include sophisticated traveller information, improved traffic flow management systems, and state-of-the-art public transportation systems. Therefore, free-flowing traffic is crucial for the development of faster transportation and connectivity systems. The majority of techniques used for predictingtraffic flow are heavily dependent on machine-learning models which require minor manual interference and consume a lot of time depending on the machine-learning model and the traffic data obtained [9; 10]. In more than 20 years of traffic-flow-prediction research, most studies are either classified as model or data-driven, or they can be both. Model-driven methods are also known as parametric techniques, e.g., time series models. However, using this type of parametric technique needs many parameters and assumptions to be used in the transportation network, which makes the performance of the traffic-flow prediction not successful. In recent years, with the technological development of transportation infrastructures, various types of traffic-data-collection methods, notably traffic-flow monitoring equipment and loop detectors, have offered an enormous amount of traffic data for predicting traffic flow. Traffic-data methods, such as artificial neural networks, recurrent neural networks, fuzzy inference systems, genetic algorithms, and particle swarm optimization can be categorized into machine- or deep-learning depending on the traffic flow of vehicles [9; 10; 11; 12; 13; 14; 15]. Well-known machine-learning techniques are inadequate when evaluating high-dimensional traffic data and when depending on comprehensive engineering. According to what was stated by [4; 5; 6], by 2050, there will be more than 7 billion people staying in metropolitan areas, which will comprise between two-fourths and two-thirds of the human population. The concept of smart cities is one of the most well-known researched ideologies for future developed urban cities. Smart cities use the integration of urban resources via the application of artificial-intelligence technology associated with big data, cloud computing, deep neural networks, and the Internet of Things (IoT) [16; 17; 18; 19]. The smart city framework can assist in city governance, effective regulations, maintenance of the cities' transportation infrastructures, and reduction of air and noise pollution [20; 21; 22]. Furthermore, the intelligent-city framework can offer people a more liveable and intelligent-driven living environment [23]. An intelligent transportation system (ITS) is an integral aspect of an innovative city framework. An ITS has an effective integration of both on-road and off-road resources by applying technologically innovative information and communication technology. ITSs are used in road transportation systems to provide real-time traffic-flow information, effective freeway traffic control, and an efficient vehicular cloud framework [24].