## 3 Research Methodology
### Description of the Hybrid CNN-LSTM Model  
In this section, we detail the use of a hybrid CNN-LSTM model for predicting CO2 emission rates. This model was specifically designed to address the unique challenges of emission forecasting in complex urban environments, where CO2 emissions depend not only on the geographical characteristics of road segments but also on the temporal evolution of traffic.  
CNNs capture spatial dependencies between road segments, such as network density, congestion, and interactions between adjacent streets. In our study, CNNs help model the local characteristics of roads based on their geographical position, providing a fine-grained representation of the spatial structure of the road network. However, CO2 emissions also vary depending on traffic fluctuations over time, which requires accounting for temporal dynamics. LSTMs were therefore integrated to capture these temporal dependencies, modeling traffic variations, congestion periods, and changes in driving patterns across different periods (daily, weekly, etc.).  
The CNN-LSTM hybrid addresses these challenges by simultaneously processing spatial and temporal information. This ability to capture spatio-temporal dynamics is crucial for predicting CO2 emissions, as it allows us to model the complex variations from one road segment to another while considering changes over time. Unlike a sequential approach, where a CNN is used for spatial feature extraction followed by an LSTM for temporal modeling, our hybrid architecture jointly exploits spatial and temporal relationships at each learning stage. This integration captures cross-interactions between local characteristics (spatial dependencies) and sequential dynamics (temporal dependencies), thereby improving the granularity and accuracy of predictions.  
In the following Research Methodology Section, we demonstrate through experimental scenarios that this integrated architecture outperforms the performance obtained by sequentially using separate CNN and LSTM models. These scenarios show that using two separate models leads to information loss during the transition from spatial features to temporal modeling, impacting the quality of the final predictions. In contrast, the hybrid model optimizes information transfer across layers by processing the data simultaneously and cohesively, resulting in a more refined CO2 emissions modeling and significantly reducing prediction errors.  
To enhance this approach, we also integrate a generative AI technique [23], which uses initial CO2 predictions to generate new input data. These new data, in the form of CO2 maps, are then reinjected into the model to refine predictions further. This allows us to simulate different traffic scenarios and better capture the effects of traffic variationson CO2 emissions at a fine scale. Figure 3 below illustrates how the initial predictions are used to generate these maps, which are then reintroduced into the model to improve estimation robustness.  
Furthermore, we employed the root mean squared error (RMSE) as the loss function during model optimization. RMSE is derived by taking the square root of the average squared differences between predicted and actual values, thereby providing an error measure that is in the same units as the target variable. By minimizing RMSE, we ensure that large deviations receive a stronger penalty and make the model's performance more interpretable. This approach effectively guides the learning process towards more stable and precise CO2 emission predictions.  
In conclusion, combining the CNN-LSTM hybrid model with a generative AI [32] approach addresses the inherent challenges of CO2 emission prediction in urban contexts. This model captures the complex interactions between road segments and temporal traffic variations, offering fine-grained and localized emission predictions essential for more robust and informed environmental analyses.