Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications

Mahmoud Elshabrawy

doi:https://doi.org/10.54216/MOR.030205

Review Article

Volume 3 • Issue 2 • PP: 21–37 • 2025

Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications

Mahmoud Elshabrawy Mohamed ^1*

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¹Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA

* Corresponding Author.

DOI https://doi.org/10.54216/MOR.030205

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Received: November 23, 2024 Revised: December 27, 2024 Accepted: January 17, 2025

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Abstract

Forecasting carbon dioxide emissions from vehicles is essential for sustainable transportation, climate-aware urban planning, and intelligent environmental management, as road transport contributes substantially to energy-related greenhouse gas emissions. Rapid growth in vehicle ownership, mobility demand, fuel use, freight activity, and congestion has exposed the limits of conventional emission-estimation methods, particularly when nonlinear and multi-factor relationships must be represented. This review examines how artificial intelligence and machine learning support vehicle carbon dioxide emission forecasting through data-driven models, deep learning, feature selection, hyperparameter optimization, and metaheuristic algorithms. It emphasizes the value of integrating traffic volume, vehicle attributes, fuel type, speed patterns, road conditions, temporal behavior, weather variables, and energy-consumption indicators to improve prediction reliability under real-world conditions. Optimized forecasting models can support emission monitoring, mitigation planning, traffic management, and low-carbon mobility policies, although challenges remain in data availability, interpretability, uncertainty, regional transferability, computational cost, and transparent validation.

Keywords

Vehicle carbon dioxide emissions Emission forecasting Artificial intelligence Machine learning Sustainable transportation

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Mohamed, Mahmoud Elshabrawy. "Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications." Metaheuristic Optimization Review, vol. Volume 3, no. Issue 2, 2025, pp. 21–37. DOI: https://doi.org/10.54216/MOR.030205

Mohamed, M. (2025). Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications. Metaheuristic Optimization Review, Volume 3(Issue 2), 21–37. DOI: https://doi.org/10.54216/MOR.030205

Mohamed, Mahmoud Elshabrawy. "Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications." Metaheuristic Optimization Review Volume 3, no. Issue 2 (2025): 21–37. DOI: https://doi.org/10.54216/MOR.030205

Mohamed, M. (2025) 'Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications', Metaheuristic Optimization Review, Volume 3(Issue 2), pp. 21–37. DOI: https://doi.org/10.54216/MOR.030205

Mohamed M. Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications. Metaheuristic Optimization Review. 2025;Volume 3(Issue 2):21–37. DOI: https://doi.org/10.54216/MOR.030205

M. Mohamed, "Artificial Intelligence-Based Forecasting of Vehicle Carbon Dioxide Emissions A Review of Machine Learning Models, Optimization Strategies, and Sustainable Transportation Applications," Metaheuristic Optimization Review, vol. Volume 3, no. Issue 2, pp. 21–37, 2025. DOI: https://doi.org/10.54216/MOR.030205

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