## 3 Research Methodology
### Description of Our Hybrid CNN-LSTM Model Architecture  
Our hybrid model architecture for predicting CO\({}_{2}\) emissions in Paris traffic leverages the strengths of both CNN and LSTM networks, enabling the effective capture of complex interactions between spatial and temporal features, resulting in more accurate predictions of CO\({}_{2}\) emissions in road traffic. Figure 5 below illustrates how temporal and spatial features are separated, processed independently, and merged to obtain a final prediction.  
* Temporal features include time, day of the week, traffic flow (q), occupancy rate (k), distance, and other traffic indicators (etat_traffic_*). An LSTM model processes them. This model consists of an input layer that takes sequences of variable length, followed by three successive LSTM layers with 50 units each, where the first layer returns the entire sequence, and the third layer returns only the last output. Each LSTM layer is followed by a dropout layer to reduce overfitting;
* Spatial features include start and end coordinates (location_start_x, location_start_y, location_end_x, location_end_y) and other spatial indicators. They are processed by a dense model simulating a CNN for vectors. This model starts with an input layer taking a feature vector, followed by a dense layer with 128 units and ReLU activation, another dense layer with 64 units and ReLU activation, and a dropout layer to prevent overfitting.  
The outputs from the LSTM and dense models are then merged using a concatenation layer, and the final prediction of CO\({}_{2}\) emissions is obtained through a final dense layer. This architecture effectively captures complex interactions between temporal and spatial features, thus offering a more accurate and robust prediction [3]. To ensure optimal training quality, we performed data normalization and missing value imputation prior to model  
Figure 5: Hybrid Architecture of Our CNN-LSTM Model.  
training. These preprocessing steps reduce scale discrepancies between variables, properly capture temporal cycles, and ensure a complete and consistent dataset for the model:  
* Data Normalization: We normalized key quantitative variables (e.g., traffic flow q, occupancy rate k, and distance) and spatial coordinates as well as transformed temporal features (hour, day) into sinusoidal representations. This approach prevents any single variable from disproportionately influencing the training process and enhances the model's stability and convergence;
* Missing Value Imputation: The initial dataset contained missing values, particularly for traffic flow and occupancy rate. We implemented a multi-tiered imputation strategy that combines linear interpolation, multivariate methods (MICE), and road network-based inference. This approach reconstructs coherent time series while preserving spatial and temporal consistency, ensuring that data gaps do not bias the model.  
These preprocessing measures result in a more balanced, complete, and homogeneous dataset, thereby improving the accuracy and robustness of our CO2 emission rate predictions. In conclusion, this hybrid architecture leverages the strengths of LSTM networks to capture temporal dependencies and CNN (dense) networks to extract spatial features, allowing for more precise modeling of CO2 emissions in an urban traffic context.