## 3 Research Methodology
### Testing and Performance Analysis of Our Hybrid CNN-LSTM Model
#### Configuration 2
Samples were organized into coherent temporal segments (e.g., weekly intervals), ensuring that the model receives temporally ordered data. Temporal variables were formatted into sequences, allowing the LSTM layers to identify recurring patterns over time. Additionally, a thorough normalization of the input variables ensured that traffic-related measures (such as flow and occupancy) share comparable scales, facilitating the model's convergence and enhancing the quality of feature extraction. Finally, the outputs of the temporal and spatial branches were merged via a concatenation layer, and a single-unit dense layer produces the CO2 emission prediction. By incorporating these improvements additional LSTM layers, enhanced regularization of dense layers, temporally coherent data structuring, and rigorous normalization, our model achieves more stable convergence and significantly improved predictive accuracy. With the final model configuration (Figure 11), we significantly improved predictive performance. As shown in Figure 12 below, the learning curve demonstrates that the model's training loss stabilized around the 30 th epoch, indicating that the network effectively converged. This stable convergence reflects a better balance between complexity and generalization, allowing the model to capture the intricate spatio-temporal patterns of CO2 emissions in urban traffic. The resulting R2 score of approximately 91% and an RMSE of 0.086 highlights the robustness and accuracy of the model in approximating actual emission values. Figure 11: Architecture of the Third Configuration of the Prediction Model. A more detailed evaluation is presented in Figure 13. In Figure 13, the strong correspondence between actual and predicted CO2 emissions across various road segments demonstrates the model's effectiveness in capturing complex, traffic-related emission dynamics. In Figure 13, the close clustering of points around the reference line indicates that the predictions align closely with observed values, confirming that the architectural enhancements and data preprocessing steps significantly improved predictive accuracy. In summary, the enhancements to the LSTM and CNN components along with more effective data preprocessing and hyperparameter tuning led to a model that not only converges more efficiently but also produces highly accurate and reliable predictions of CO2 emissions. This final configuration represents a substantial step forward in our approach to forecasting urban traffic-related emissions. Overall Comparison of Different Versions of Our Hybrid Model  
To evaluate the evolution of our hybrid CNN-LSTM model's evolution, we provide a visual and tabular comparison of the three configurations. Figure 14 illustrates the training loss curves, showing the progressive improvement in the model's convergence and accuracy. Additionally, Table 2 summarizes the key modifications made to the input data, data preprocessing, layer architecture, and hyperparameters for each configuration, alongside their respective impacts on predictive performance. Figure 12: Learning Curve of the Third Prediction Model Configuration with an R2 score of 91%. Figure 13: Predicted vs. Actual Values Analysis for Configuration 3: (**a**) Line plot comparing actual and predicted CO2 emissions; (**b**) scatter plot showing prediction errors with a reference line. The training loss curves reveal a clear progression in the stability and optimization of the model. Each configuration builds upon the lessons learned from the previous iterations, refining the architecture and training approach to achieve better predictive accuracy. The progression of our model configurations is summarized in Table 2, highlighting the input data, preprocessing strategies, model architectures, training durations, and corresponding R\({}^{2}\) scores. Each configuration reflects iterative enhancements aimed at improving the predictive performance of our hybrid CNN-LSTM model.