## 3 Research Methodology
### Testing and Performance Analysis of Our Hybrid CNN-LSTM Model
#### Configuration 2  
We proposed a new configuration in this second phase, retaining the model architecture from Configuration 1 but modifying the hyperparameters and input data. First, we normalized certain data to help the different layers of our model better understand the correlations between traffic data and CO2 emission rates. For example, for temporal data,  
Figure 8: Predicted vs. Actual Values Analysis for Configuration 1: (a) Line plot comparing actual and predicted CO2 emissions; (b) scatter plot showing prediction errors with a reference line. Figure 7: Learning Curve of the First Prediction Model Configuration with a an R2 score of 0.51.  
we used cosine and sine normalization functions to represent the hours and days of the week, providing more structured information to our model. As shown in Figure 7, the previous model had yet to finish learning and had yet to reach the local minimum. Therefore, we increased the number of training epochs to allow more profound knowledge and better identification of correlations between the data. The results showed an improvement with an accuracy of 61% after making these modifications, as illustrated in Figures 9 and 10. However, although the results were better, they needed to be more satisfactory. By analyzing Figure 10, which compares predicted values to actual values, we found that the model's predictions could have more perfectly followed the ideal prediction line (red line). The dispersion of points around the prediction line indicated that the model had yet to correctly capture all relationships between the input variables and the target. In other words, the predicted values did not match the actual values well enough, suggesting that the model still required adjustments to improve accuracy and robustness. The points in Figure 10 show that although the predictions generally followed the trend of the actual values, significant variation was still needed in the model. This variation indicates that the model had yet to thoroughly learn the complex relationships between the input data and CO2 emission rates. Consequently, we decided to repeat the process of optimizing our model's architecture in a new cycle to improve performance further. The following adjustments focused on finetuning hyperparameters and model structure to reduce prediction dispersion and align predicted values more closely with actual values. In conclusion, although Configuration 2 showed significant progress over Configuration 1, the results indicate that further adjustments were necessary to achieve optimal accuracy. The  
Figure 10: Predicted vs. Actual Values Analysis for Configuration 2: (**a**) Line plot comparing actual and predicted CO2 emissions; (**b**) scatter plot showing prediction errors with a reference line. Figure 9: Learning Curve of the Second Prediction Model Configuration with a an R2 score of 0.61.  
following optimization cycle aimed to refine the model's architecture and hyperparameters to achieve even better performance. Configuration 3  
In this final configuration, our model fully leverages the complementarity between temporal and spatial dimensions. The temporal branch now consists of three sequential LSTM layers: two initial layers with 100 units each, both accompanied by dropout layers (rate 0.3) to mitigate overfitting, followed by a third LSTM layer with 50 units. This arrangement enables a more nuanced analysis of traffic sequences, taking into account daily and weekly variations, and thereby capturing longer-term trends. On the spatial side, the architecture includes two dense layers: The first, with 128 units, employs a ReLU activation and L2 regularization (0.01), followed by a dropout layer (rate 0.5) to further prevent overfitting. The second dense layer, with 64 units, also uses ReLU activation and L2 regularization (0.01). Together, these layers provide a more robust and stable spatial representation of the road segments. In parallel, the data were structured to support sequential learning.