## 3 Research Methodology
### Testing and Performance Analysis of Our Hybrid CNN-LSTM Model  
#### 3.5.1 Introduction of Optimization Methods in Our Cyclic Optimization Phase  
This section presents the various steps that allowed us to adjust our hybrid CNN-LSTM model during its training. Through these progressive adjustments, we improved the accuracy of our model from 61% to 91%. We underwent several modification, comparison, and evaluation cycles before achieving this optimal result. To achieve this, we conducted a comparative study on the several following aspects of our architecture:  
* Data Normalization: Data normalization is a crucial step in enhancing the learning of our CNN-LSTM model. We identified several important features to normalize: em (a) Quantitative Features: Features such as traffic flow (q), occupancy rate (k), and distance were normalized to ensure they were on a comparable scale. This allowed us to reduce the magnitude of differences between these features, facilitating the learning process of our model; (b) Temporal Features: Temporal data, particularly hours and days of the week, were transformed into their sinusoidal representations (sine and cosine) to capture daily and weekly cycles. This transformation enabled the model to understand better and predict the temporal variations in CO2 emissions; (c) Location Coordinates: The start and end coordinates (location_start and location_end) were normalized to ensure a uniform scale. This allowed the model to process location data more effectively, reducing the effects of different geographical scales; (d) Categorical Features (traffic state and day type): We also applied categorical encoding to certain variables for better interpretability. The traffic state (etat_trafic) was classified into four discrete levels representing different road traffic conditions: 1 for fluid traffic, 2 for pre-saturated traffic, 3 for saturated traffic, and 4 for blocked traffic. We also introduced a binary feature indicating whether the data point was recorded on a weekday or weekend. This categorization provided the model with structured information about traffic intensity and temporal context, enhancing its ability to learn complex patterns related to CO2 emissions.  
Normalizing these data had a significant impact. It improved the model's stability and convergence speed during training, leading to better overall performance. Due to the different scales and feature variations, the model would have struggled to learn consistently without this normalization.
* Model Hyperparameters: We targeted the hyperparameters of our model to improve accuracy while avoiding overfitting. During this phase, we monitored the loss function value throughout the learning process and adjusted the following parameters: em
* Number of Epochs: Optimizing the number of epochs was crucial to avoid overfitting. Too many epochs can lead to overfitting the training data, while too few can result in underfitting;
* Number of Layers: Adding additional layers allowed us to capture more complex features from the input data, enhancing the model's representation capability;
* Dropout Layers: We integrated dropout layers to reduce overfitting by randomly turning off specific neurons during training. This technique improved the model's generalization capability;
* Number of Units per Layer: Adjusting the number of units in each layer helped us balance representation capacity and generalization ability, thereby optimizing the model's overall performance.  
By implementing these optimization methods, we significantly enhanced the accuracy and robustness of our hybrid CNN-LSTM model, enabling more precise and reliable predictions of CO2 emissions in Paris traffic.