Which One is Better? Distributed Artificial Intelligence Strategies for Accurate Vehicular Emissions Forecasting

The rapid expansion of urban vehicular networks has led to increasing carbon emissions, posing significant environmental challenges in densely populated areas. Accurate emission predictions are crucial for sustainable urban planning, but current methods face limitations in handling large-scale dynamic data while balancing latency, privacy, and communication efficiency. This paper proposes a comprehensive framework study that compares centralized, federated, and shared learning approaches for CO2 emissions prediction in vehicular networks, using data from vehicles and roadside units (RSUs) to predict emissions in diverse urban scenarios. By evaluating the performance of each approach on latency, communication overhead, and prediction accuracy, this work provides insights into optimizing learning strategies for real-time, scalable, and privacy-preserving emissions management in intelligent transportation systems. The findings offer valuable guidance to urban planners and policymakers, fostering the development of sustainable urban mobility solutions.