## 1 Introduction
Consequently, researchers have turned their attention to machine learning techniques, particularly DRL, to address the limitations of traditional control methods. DQN [7; 10], a prominent DRL algorithm, has garnered significant interest due to its ability to learn optimal policies directly from raw sensory input data. Initially proposed for Atari games by [PERSON] et al. [11], DQN combines deep neural networks with Q-learning, enabling the direct learning of complex behaviors from high-dimensional input spaces. Researchers leveraging this approach aim to develop an advanced system capable of autonomously regulating vehicle speeds across various driving scenarios. In the study conducted by [PERSON] et al. in 2024, they tried to solve the traffic congestion problem by using 6G communication technologies and DRL [12]. As a result of their study, they stated that they reduced the travel time in traffic by 28.2%. In a study conducted in 2024, [PERSON] et al. proposed a DRL-based method to improve human and vehicle evacuation in parking lots during emergencies [13]. They stated that as a result of their work, they reduced the evacuation time by 7.75%. In another study conducted by [PERSON] et al. in 2024, they proposed a method that provides traffic management using reinforcement learning vehicle-to-vehicle (V2V) connection technologies [14]. In their study, they also stated that they achieved better performance than human drivers in terms of safety, efficiency, comfort and fuel consumption. In their study conducted in 2024, [PERSON] and [PERSON] proposed a method using DRL to relieve traffic congestion in mixed traffic consisting of connected autonomous vehicles and human-operated vehicles [15]. They stated that their proposed method significantly reduces non-repetitive congestion on the highway. [PERSON] et al. used the DRL method while proposing a driving strategy for connected autonomous vehicles in a traffic scenario with humans and connected autonomous vehicles in their study conducted in 2024 [16]. They stated in their study that they developed a method to avoid congestion for connected autonomous vehicles. Numerous studies in the literature have proposed optimal speed recommendations for vehicles. The majority of these studies were conducted using the green light optimal speed advisory (GLOSA) algorithm. Many models have been developed for autonomous driving using AI in recent years. Some published studies, their methods and their objectives are shown in Table 1.  
In the studies given in Table 1, the linear regression method was used to calculate the optimum speed for vehicles in the study conducted by [9]. DRL methods [17; 18; 19; 20; 22], which have become popular in recent years, are being used for autonomous driving models, and successful results are being achieved. Another area where DRL is commonly used is the development of Autonomous Driving Policies for vehicles on highways [17; 18; 19; 20; 21; 22; 23]. The studies listed in Table 1 identify their target audience as connected vehicles or vehicles with autonomous driving capability. Urban traffic management is pivotal in reducing fuel consumption, CO\({}_{2}\) emissions and enhancing driving conditions in cities, all of which contribute significantly to environmental sustainability. Traditional traffic control methods often struggle to cope with the complexity of modern urban transportation networks, leading to inefficiencies, congestion and rising environmental impacts. In recent years, DRL has shown promise in optimizing traffic flow and addressing environmental challenges by leveraging its ability to learn optimal control policies in dynamic, complex environments. The primary objective of this research is to assess the effectiveness of DRL algorithms in urban traffic management and their role in fostering environmental sustainability while enhancing the driving experience. In this context, the study seeks to address the following research questions:  
1.