## 1 Introduction  
Cities are among the largest and most complex structures in human history. While these densely populated urban areas facilitate life, they also bring significant challenges. One such challenge is the environmental pollution caused by transportation, an essential component of modern life. According to the International Energy Agency (IEA), the global energy consumption of the transportation and industrial sectors was approximately 30%, yet together, these sectors accounted for approximately 46% of total carbon emissions in 2021. Interestingly, while energy consumption and emission trends have remained relatively stable since 2011, the transportation sector's impact has continued to increase [1]. Air pollution is directly linked to heavy vehicle traffic in urban areas. Harmful particles and gases emitted from exhaust contribute to respiratory diseases, cardiovascular disorders and even cancer. Additionally, these emissions, including greenhouse gases such as carbon dioxide (CO\({}_{2}\)), nitrogen dioxide (NOx) and carbon monoxide (CO), exacerbate climate change and disrupt the environmental balance. The constant stop-and-go movement of cars and other vehicles further amplifies emission levels and prolonged time spent in traffic exacerbates this problem. The environmental pollution and emission issues associated with urban traffic are becoming increasingly urgent. Efforts to develop new emission sources continue to grow [2]. However, addressing the existing fleet of vehicles and equipment is also crucial for significantly reducing environmental pollution. In their 2023 experimental study, [PERSON] and [PERSON] examined the impact of sudden changes in travel time and vehicle speeds on fuel consumption and environmental pollution in real-world traffic scenarios [3]. They found that waiting times in traffic contribute to approximately 50% of fuel consumption and environmental pollution. Consequently, traffic management is not only a daily inconvenience but also critical for the sustainability and livability of cities. Technological advancements, including artificial intelligence (AI), offer new perspectives for solving these challenges. DRL, a subset of AI, has gained significant attention due to its potential to develop eco-friendly solutions in traffic management and transportation systems. DRL algorithms, guided by data and environmental feedback, adapt, learn and optimize real-time traffic control strategies [4; 5; 6]. In a 2022 study, [PERSON] et al. proposed an adaptable speed-planning method for connected and autonomous vehicles [7]. Their trained DRL agent could adapt to variable traffic light scenarios and quickly solve for an approximate optimal speed trajectory. Another study in 2020 applied the existing Markovian traffic assignment framework to new traffic control strategies [8]. Initially, they performed classical Markovian static assignments and then transitioned to dynamic traffic assignments using the Simulation of Urban Mobility (SUMO) simulation program. Their work focused on teaching routing behavior at intersections and highlighted the scalability potential of Markovian chain theory for larger networks. In a 2021 thesis, DRL was used to optimize urban signal networks [5]. The proposed approach outperformed the classical DQN algorithm in terms of results. Similarly, in another 2020 study, machine learning was employed to recommend vehicle speeds [9]. The researchers used linear regression under supervised learning to train the model and demonstrated that their proposed algorithm achieved better accuracy and performance than existing state-of-the-art algorithms. Ensuring optimal speed control is vital for enhancing road safety, minimizing traffic congestion and improving fuel efficiency. Traditional vehicle speed control systems rely on rule-based algorithms or classical control methods based on open information and predefined rules. However, these approaches often struggle to adapt to dynamic and complex driving scenarios that require real-time learning and responsiveness.