## 5 Discussion: Model Advantages, Limitations, and Generalizability  
* Model Accuracy: The CNN-LSTM hybrid model provides highly accurate predictions of CO\({}_{2}\) emissions, as evidenced by the slight difference between actual and predicted values. This performance is achieved by combining spatial-temporal features captured by the CNN with temporal sequences modeled by the LSTM, allowing the model to handle the complex dynamics of road traffic in Paris effectively;
* Impact of Traffic Parameters: The model effectively captures the effects of various traffic-related factors on CO\({}_{2}\) emissions, including traffic flow, occupancy rate, road location, congestion, and peak hours. The analysis shows a direct correlation between the increase in these factors and CO\({}_{2}\) emissions, accurately demonstrating the model's ability to represent different traffic conditions, whether fluid or congested, accurately;
* Practical Applications: The model is ready for real-world applications, particularly in GreenNav, our ecological navigation application. It can provide route recommendations optimized based on real-time CO\({}_{2}\) emission forecasts, contributing to urban traffic management and emission reduction policies;
* Data Dependency: The model's limitation is its reliance on a significant amount of precise spatiotemporal data to capture traffic variations. The quality and coverage of the training data directly impact the accuracy of the model's predictions;
* Adaptability to Other Urban Environments: Although our model has been precisely calibrated and trained using data from the Paris road network, it is designed to be modular, allowing it to adapt to different urban contexts. We propose two complementary strategies to generalize its application to cities with diverse road network characteristics and traffic behaviors: transfer learning and federated learning: em  
* Transfer learning [38] is beneficial when only a limited amount of data is available for the target city. In our context, the model's convolutional layers (CNN), which capture the spatial features of the road segments, can be reused as-is since many cities share similar spatial patterns, such as road intersections and density. The LSTM layers, which capture temporal dependencies, can then be retrained to reflect the unique temporal dynamics of traffic in the new urban environment. For instance, if our model were to be applied to the city of Lyon, we could retain the CNN layers and only retrain the LSTM layers to adapt to Lyon's specific temporal traffic patterns, such as peak hours and weekend behaviors. This approach would enable a quick adaptation to the local context without requiring extensive new data, thereby maintaining high performance on critical traffic segments;
* On the other hand, federated learning [39] is more suitable for large metropolitan areas or cities that differ significantly from Paris regarding road network density, driving behavior, or vehicle types. In this scenario, the model is trained collaboratively using data from multiple cities while ensuring data privacy. Each city trains the model locally using its data, and only the model's updated parameters are shared, allowing for creating a global model without transferring sensitive information. For example, if we wanted to adapt the model for NewYork City, factors such as road congestion, speed distribution on major highways, and seasonal variations could be incorporated through federated learning without exposing Paris or New York's local data. This would allow the model to generalize its understanding across multiple urban environments, making it more resilient to varying geographical conditions;
* To enhance both the reproducibility and scalability of our approach, we recommend following a standardized protocol: replicate the preprocessing steps (normalization, imputation, and temporal transformations) for the new city, load the pre-trained CNN weights, and then retrain the LSTM layers using locally collected data.