## 1 Introduction
How can DRL algorithms, particularly the DQN-based model, be applied to optimize fuel consumption, reduce emissions and improve driving comfort and safety across various traffic scenarios? 2. To what extent can the proposed DQN-based model produce effective results using existing traffic data and how adaptable is it to different traffic conditions, in terms of both environmental impact and user experience? \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Article** & **Used Methods** & **Purpose of the Article** \\ \hline [PERSON] and others [9] & Linear Regression & Speed recommendations for vehicles \\ [PERSON] and others [17] & DDQN & Autonomous Driving Policy on the highway \\ [PERSON] and others [18] & DQN & Creating a driving policy by adding multiple traffic light information for connected vehicles \\ [PERSON] and others [19] & DQN + deterministic policy gradient & Autonomous Driving Policy \\ [PERSON] and others [7] & DQN + deterministic policy gradient & Creating a driving policy by adding multiple traffic light information for connected vehicles \\ [PERSON] and others [20] & DQN + CNN & Autonomous Driving Policy \\ [PERSON] [21] & MTD3, improved version of TD3 & Autonomous Driving Policy \\ [PERSON] and others [22] & New Soft Actor-Critic (SAC) & Autonomous Driving Policy \\ [PERSON] and others [23] & DDQN & Autonomous Driving Policy on the highway \\ [PERSON] and others [24] & Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) & A vehicle acceleration prediction model \\  & & behavior analysis based on machine learning \\  & & methods and driving is proposed. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reviewed articles. 3. How does the performance of the DQN-based model compare to other methods in the existing literature in terms of fuel consumption, emissions, driving comfort and safety? 4. Can the proposed method be applied to vehicles driven by human drivers and what is its potential to improve traffic flow, reduce fuel consumption, enhance driving comfort and increase safety in real-world traffic conditions? In response to these research questions, the following hypotheses are proposed:  
* Hypothesis 1: The DQN-based model can effectively optimize fuel consumption, reduce CO2 emissions and improve driving comfort and safety by learning optimal driving policies in various traffic scenarios. * Hypothesis 2: The DQN-based model, when trained with existing traffic data, can produce reliable results in optimizing fuel consumption, reducing emissions and improving driving comfort and safety, and it can be generalized to various urban traffic conditions. * Hypothesis 3: The DQN-based model will outperform other DRL methods or traditional traffic optimization techniques in terms of fuel consumption reduction, environmental impact and enhancements in driving comfort and safety. * Hypothesis 4: The proposed DQN-based method can be applied to vehicles with human drivers, improving traffic flow, reducing congestion, decreasing fuel consumption and enhancing driving comfort and safety in real-world driving situations. This study aims to explore the impact and significance of the proposed DQN-based method for urban traffic management, emphasizing its potential to foster sustainable urban mobility, improve driving comfort and safety and contribute to a more efficient transportation system. The main contributions of the study can be summarized as follows:  
* Using different scenarios, the proposed method's performance in reducing fuel consumption, emission release and environmental pollution was demonstrated. * Recently popular DRL algorithms such as DQN, Double Deep Q-Network (DDQN), Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) were trained on the same scenarios, and their results were compared in terms of reducing fuel consumption, environmental pollution and emission release; it is thought that the proposed method is more successful and will contribute to this area.