Estimation of co2 emissions in transportation systems using artificial neural networks, machine learning, and deep learning: A comprehensive approach

Abstract

This study focuses on estimating transportation system-related emissions in CO(2 )eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey's climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey's road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field.