## 3 Research Methodology  
### Data Structure  
Before detailing the development of our hybrid prediction model, it is essential to present the dataset structure used for training our model. Proper structuring is a critical element of our quest to obtain reliable predictions. Our dataset includes vehicle specifications, traffic, and environmental conditions. Figure 1 below illustrates the composition of this dataset:  
* The first source is OpendataParis [27], an API that provides real-time information about Paris's roads. These data include road names, locations, traffic flow, occupancy rate, road traffic conditions, and recording dates, offering an overview of the traffic status;  
Figure 1: Description of our Dataset.  
* The second data source is Google API [28], which provides additional important information such as road distances, average speeds, and speed limits, adding an essential dimension to our dataset;
* The third component is GreenNav Algo, our probabilistic algorithm for calculating the CO2 emission rate for each road in Paris. This algorithm allows us to generate precise estimates of CO2 emissions based on real-time traffic conditions;
* Finally, we use data from the Airparif API [29] and the Getambee API [30], which provide information on air quality in Paris. Additionally, the statistical algorithm developed by ADEME integrated into the Airparif platform allows us to calculate the CO2 emission rate between two specific positions [31]. By combining these different sources, we built a rich and diverse dataset essential for the training and performance of our hybrid CNN-LSTM model.  
Figure 2 is an example of our integrated dataset after merging all sources. This sample corresponds to Boulevard de Belleville at 3:00 p.m. on 1 January 2024.  
After collecting and structuring our data, the next step was to assign a reference CO2 emission value to each road segment. Each segment is thus considered as an individual CO2-emitting unit. To this end, we developed a probabilistic algorithm that estimates, on an hourly basis, the CO2 emissions produced by every segment. This estimation is based on traffic data, vehicle characteristics, and various emission factors. The following equation represents the modeling:  
Figure 2: Example of the final integrated dataset after merging data from OpendataParis, Getambee, Google API, and Airparif.  
\[E_{segment}=D\times\sum_{i=}^{N}FE_{i} \tag{1}\]  
where  
\(E_{segment}\) is the total CO2 emissions for the segment (in g/h).  
\(D\) is the segment distance (in km).  
\(N\) is the number of vehicles passing through the segment during the given period.  
\(FE_{i}\) is the emission factor of the ith vehicle, encompassing parameters such as the vehicle's environmental class, maintenance status, driving behavior (aggressive or not), air conditioning/heating use, and cold starts.  
This calculation provides the hybrid CNN-LSTM model with a coherent reference dataset of CO2 emissions per segment by incorporating these emission factors and traffic conditions. These values serve as a training base, allowing the model to establish correlations between traffic characteristics and emission intensity, thereby improving the accuracy of its predictions.