## 4 Discussion  
This study presents a DQN model designed to optimize traffic flow and reduce environmental impacts in urban traffic systems. The findings provide significant evidence supporting the proposed hypotheses. Unlike predictive traffic models, the DQN approach focuses on real-time data from current traffic conditions and simulations, providing op  
Figure 7: Reward, fuel consumption and emission values obtained as a result of the training. (**a**) First scenario. (**b**) Second scenario. (**c**) Third scenario.  
timal speed management decisions for each situation. This methodology demonstrates significant potential in minimizing CO2 emissions and fuel consumption across various scenarios. Compared to other DRL methods, the DQN algorithm consistently outperforms in achieving lower emission levels, highlighting its robustness in handling complex traffic conditions. By reducing stop-and-go movements, the proposed model enhances traffic flow and contributes to sustainable urban mobility objectives, such as smoother speed transitions, reduced abrupt accelerations or decelerations and indirectly improved driving comfort and safety. Although the study did not directly measure driving comfort and safety, the observed reduction in traffic congestion suggests qualitative benefits in these areas. Additionally, the economic incentives provided by reduced fuel consumption, along with the environmental advantages of lower emissions, underscore the broader impact of this method. For real-world application, integrating the model with cloud-based data from urban traffic control systems such as traffic light phase durations, density and queue lengths would be essential. As many urban traffic systems already store such data, sharing it via APIs could streamline the deployment of this method. Furthermore, equipping vehicles with onboard trip computers containing Global System for Mobile Communications (GSM) modules would enable real-time data reception and optimization. However, implementing such systems introduces challenges, including the necessity of data sharing between traffic management centers and vehicles, as well as the additional costs of integrating trip computers in non-autonomous, human-operated vehicles. The target audience for this model includes vehicles lacking autonomous driving capabilities and vehicle-to-vehicle communication. Despite these limitations, the findings demonstrate the potential of a DRL-based approach in improving traffic flow, reducing emissions and offering a safer and more comfortable driving experience. Future research could enhance this model by incorporating time series forecasting to enable predictive optimization. Exploring the integration of forecasting methods with the DQN algorithm could further improve traffic flow and environmental outcomes. While the study underscores the significant contributions of DRL algorithms in determining optimal speeds, it also highlights the need to overcome implementation challenges, such as cost and infrastructure requirements, to fully realize their potential. Ultimately, this study represents an important step toward cleaner, more efficient and sustainable urban traffic systems.