GreenNav: Spatiotemporal Prediction of CO2 Emissions in Paris Road Traffic Using a Hybrid CNN-LSTM Model

In a global context where reducing the carbon footprint has become an urgent necessity, this article presents a hybrid CNN-LSTM prediction model to estimate CO2 emission rates of Paris road traffic using spatio-temporal data. Our hybrid prediction model relies on a real-time road traffic database that we built by fusing several APIs and datasets. In particular, we trained two specialized models: a CNN to extract spatial patterns and an LSTM to capture temporal dynamics. By merging their outputs, we leverage both spatial and temporal dependencies, ensuring more accurate predictions. Thus, this article aims to compare various strategies and configurations, allowing us to identify the optimal architecture and parameters for our CNN-LSTM model. Moreover, to refine the predictive learning evolution of our hybrid model, we used optimization techniques like gradient descent to monitor the learning progress. The results show that our hybrid CNN-LSTM model achieved an R2 value of 0.91 and an RMSE of 0.086, outperforming conventional models regarding CO2 emission rate prediction accuracy. These results validate the efficiency and relevance of using hybrid CNN-LSTM models for the spatio-temporal modelling of CO2 emissions in the context of road traffic.