## 1 Introduction  
The increase in greenhouse gas (GHG) emissions in recent years has significantly contributed to the global warming of our planet. According to the International Energy Agency, transportation is the second most responsible sector for GHG emissions [1]. Greenhouse gases are a combination of several gases: carbon dioxide (CO\({}_{2}\)), nitrous oxides (N\({}_{2}\)O), methane (CH\({}_{4}\)), and hydrofluorocarbons (HFCs). These gases are the primary GHGs emitted by automobiles in road traffic [2, 3]. Transportation activities produce more than 75% of the total environmental CO\({}_{2}\) emissions [3, 4]. Consequently, reducing transportation emissions has become a global goal to combat climate change [3, 5]. Moreover, excessive traffic can have various undesirable impacts, including increased noise and gaseous pollution. Additionally, humans exposed to high amounts of CO\({}_{2}\) have a significantly increased likelihood of contracting various diseases and conditions, such as cancer, heart disease, respiratory problems, and premature births [3, 6, 7, 8, 9]. Therefore, estimating and predicting vehicle CO\({}_{2}\) emissions is essential to evaluating and mitigating these health and environmental impacts. Hence, several modelscan be used to anticipate CO\({}_{2}\) emissions from transportation [10; 11; 12; 13]. Early traffic CO\({}_{2}\) models are based on traditional approaches that primarily rely on data sampling and technologies, such as GPS data. These models simulate the distribution of traffic CO\({}_{2}\) emissions in a region by combining thematic maps and vehicle emission equations [3; 11; 14]. Two main approaches are commonly used to estimate CO\({}_{2}\) emissions in road traffic: the top-down approach and the bottom-up approach [12; 13]. The top-down approach uses aggregate data, such as fuel consumption or total traffic volume, to calculate emissions at a national or regional scale. While this method allows for a quick analysis of overall trends, it often lacks precision when capturing local details or smaller areas. On the other hand, the bottom-up approach relies on more detailed data, such as individual vehicle behavior or specific trip conditions. By using granular traffic and vehicle data, this method provides more accurate and localized estimates of CO\({}_{2}\) emissions, making it useful for targeted policies and urban-scale analyses. Moreover, recent approaches have introduced additional variables to refine emission estimates further. These variables include vehicle speed, vehicle-specific power (VSP), acceleration, real-time traffic congestion, weather conditions, driving behavior, road gradient, and historical CO\({}_{2}\) release rates. These newer methods enable a more comprehensive analysis of the factors influencing CO\({}_{2}\) emissions, resulting in more accurate modeling tailored to the dynamic conditions of an urban road network [11; 12; 15]. In our research [16; 17; 18], we have developed a solution capable of predicting CO\({}_{2}\) emissions by considering various road traffic parameters and spatio-temporal conditions to address this sustainable development challenge. Regarding the spatial representation of our data, road traffic can be modeled on a spatial map, as road traffic data are linked to the space they occupy. For instance, congested roads have higher emissions due to abrupt speed variations and frequent stops/restarts. This gives us an indication of the spatial occupancy of cars on a map. As for the temporal representation of our data, road traffic can also be modeled in a temporal context. Road traffic varies according to the times of the day and days of the week, which affects CO\({}_{2}\) emission levels. For example, during peak hours, there is often a significant increase in traffic congestion, leading to higher emissions. This temporal variation is crucial for understanding and predicting CO\({}_{2}\) emissions, reflecting changes in travel behaviors and traffic conditions.