## 3 Research Methodology
### Structure of the Research Methodology  
Our article addresses the following problem: How can we predict the CO2 emission rate for each street in Paris under road traffic conditions using an AI model? To achieve our objective, we organized our research pipeline as explained below and illustrated in Figure 4.  
1. Phase 1 Data Preprocessing: The first step in our methodology involves preparing and normalizing our dataset to ensure better learning by our models. We divided our dataset into two distinct sets: a temporal set and a spatial set: em 1. Temporal Set for LSTM model: Contains temporal variables such as hour_sin, hour_cos, weekday_sin, weekday_cos, traffic flow (q), occupancy rate (k), distance, and traffic indicators (etat_trafic_*). These data allow modeling the temporal variations in CO2 emissions; 2. Spatial Set for CNN model: Includes spatial variables such as location_start_x, location_start_y, location_end_x, location_end_y, traffic flow (q), occupancy rate (k), distance, and traffic indicators (etat_trafic_*). These data capture the spatial relationships between different road segments. By separating the data  
Figure 3: Integration of Generative AI in Our Research Methodology.  
into temporal and spatial sets, our two models can specialize in their respective domains, thus optimizing the learning process.
2. Phase 2 Model Training: The second phase of our methodology involves training our models: em 1. CNN Model: Designed to extract complex spatial features from our dataset; 2. LSTM Model: Designed to handle temporal dependencies in the data series. We then divided our dataset into training and testing sets to feed our data into the training and testing phases of our hybrid model: em 1. Training Set: X_train_temporal, X_train_spatial, y_train; 2. Testing Set: X_test_temporal, X_test_spatial, y_test. To leverage both models, we merged the outputs from each model to obtain a final prediction that combines spatial and temporal information. During the training phase, we relied on MSE as the loss function to evaluate and guide the optimization process of our model parameters. By systematically minimizing MSE, we ensure that our model continuously refines its predictions, converging towards a solution that better aligns with actual CO\({}_{2}\) emission values.
3. Phase 3 Model Testing and Evaluation: After training our models, the final phase is testing their performance on a test dataset. These test data are randomly selected from the dataset to evaluate the model's generalization. In this context, our task is a regression problem rather than a classification problem, so using a traditional accuracy metric (commonly employed in classification tasks) is inappropriate. Instead, we adopted the coefficient of determination (R\({}^{2}\)) as our main evaluation metric [33; 34]. R\({}^{2}\) is a standard measure for regression models that quantifies how well the predicted values approximate the real data. It represents the proportion of variance in the dependent variable that is predictable from the independent variables. Additionally, we employed statistical visualization tools to analyze learning errors and adjust our model accordingly [35; 36; 37]. This iterative process allows us to continually loop back to previous steps to enhance our model's performance.  
Figure 4: Description of Our Research Pipeline.