Integration of Data Acquisition and Modeling for Ecological Simulation of CO₂ Emissions in Urban Traffic: The Case of the City of Paris

Considering the environmental protection requirements, our study proposes a modelling of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in the city of Paris. At the core of our approach, we have developed a model based on probabilistic algorithmic equations to estimate CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions on a street-by-street basis. Our model relies on a data preprocessing process that includes significant steps of cleaning and imputation, which are essential to ensure data integrity and reliability. These data, sourced from various APIs, cover road traffic, weather conditions, and air quality. This work is crucial to refine the model and ensure the accuracy of the results. The main objective of our research is to develop dynamic models and simulations that accurately reflect real-time CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission rates for each street in Paris. We have developed a modelling method that combines environmental and urban factors to faithfully represent road traffic emissions in Paris. Consequently, our results demonstrate the potential to simulate CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, highlighting emission patterns that vary according to the time of day and atmospheric conditions and showing the influence of environmental factors on urban emissions. From a technical standpoint, the equations we created are based on a hybrid CNN-LSTM model [1] and contribute to continuous development and refinement through deep learning. This approach aims to create a reliable and robust predictive tool for simulating CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions from road traffic. By doing so, we contribute to sustainable urban planning and reduce the environmental impact of road traffic through efficient management and real-time emissions monitoring.