## 1 Introduction  
Traffic congestion is a real problem that affects urban areas around the world due to rapid urbanization and desire for private travel [1]. It occurs when demand exceeds the existing road system's capacity, causing the traffic to slow down or come to a complete halt. The consequences of traffic congestion are far-reaching and include increased commute times, energy consumption, environmental degradation, and traffic accidents [2; 3; 4]. According to a report by the Texas A&M Transportation Institute, traffic congestion costs the US economy approximately $166 billion USD per year in lost productivity and increased fuel cost. In China, traffic congestion contributes to additional air pollutants such as PM2.5, and O\({}_{3}\), leading to increased rates of premature mortality [5]. Furthermore, the World Health Organization's report on road traffic crashes shows that the deaths of approximately 1.3 million people around the world each year are linked to congestion [6]. Therefore, managing traffic congestion is a crucial area of study, and scholars from various disciplines are working on preventing congestion by analyzing big data using artificial intelligence [7] to improve the current scenario. The intelligent transportation system (ITS) is an efficient and robust traffic management system that integrates a variety of advanced technologies [8; 9] and has improved the traffic congestion problem. A crucial component of ITS is the predictive model, which estimates traffic patterns [10; 11] and predicts short- or long-term traffic conditions [12; 13; 14] using traffic data such as traffic speed, flow volume, and congestion levels. Previousresearch on traffic prediction has mainly relied on data from fixed sensors (such as road sensors, inductive loops, or traffic cameras) installed on road networks or from vehicle networks, including VANET or Floating Car systems. In [15], traffic-flow data from human-driven vehicles at road intersections was used to predict and model the road traffic flow pattern using an Artificial Neural Network. In [16], several simulation studies were conducted based on real vehicle networks and intelligent traffic communication systems to streamline the traffic density and reduce traffic congestion using wireless communication technologies such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X). However, acquiring and using such data and technologies can be costly and often requires special permission. In recent years, public web services such as Google Traffic [17], Bing Maps [18], Seoul Transportation Operation and Information Service (TOPIS) [19], and Biadu Maps [20] have started providing road-level traffic information. These web services are publicly accessible, pre-processed, and provide traffic information as a map for almost all the cities in the world, but only a few studies have utilized this type of data. In the past, the curse of dimensionality posed a challenge, as predicting traffic patterns involves processing series of images, which could be computationally expensive. However, the significant increase in computing power in recent years has made using this type of data for traffic prediction feasible. Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Convolutional LSTM (ConvLSTM) are well-known for their capability to learn temporal relationships across sequential data and have shown promising results in various applications such as recognition [21], translation, and time-series prediction [22; 23]. Convolutional Neural Networks (CNNs) are well known for their ability to extract features from image or video datasets. Many existing CNN-based algorithms are designed to go from high-resolution to low-resolution and progressively recover the original resolution using upsampling operations, which can limit their ability to capture fine-grained details and lead to suboptimal performance.