Predicting CO2 Emissions by Vehicles Using Machine Learning

Chadha, Akalbir Singh; Shinde, Yashowardhan; Sharma, Neha; De, Prithwis Kumar

doi:10.1007/978-981-19-2600-6_14

Cited by 4 publications

References 17 publications

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ML-ERV: A machine learning-based CO₂ emissions model designed for rental vehicles

Yousfi

2024

IOP Conf. Ser.: Earth Environ. Sci.

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On-road carbon dioxide (CO2 ) emissions from rental vehicles are very much affected by vehicle category, types of vehicle transmission, fuel type and traveled distance. In this paper, four machine learning models (decision trees, random forest, AdaBoost and XGBoost) were used to predict CO2 emissions from rental vehicles. The models were trained and tested on Carbookr’s bookings dataset. The results show that the traveled distance, the included mileage, the ACRISS code, the rental duration, the driver’s home country, the country where the driver works and the country where he pickups up the vehicle as well as the country where he returns the vehicle had clear impacts on the transient CO2 emission rates. There was a positive correlation between these features and CO2 emission rates. The findings also indicate that XGBoost achieves the best accuracy performance when compared to other machine learning algorithms in terms of the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE) and the Coefficient of Determination (R 2). The results show that the machine learning-based CO2 emission model ML-ERV trained using XGBoost algorithm outperforms the state-of-the-art CO2 emissions calculators.

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ML-ERV: A machine learning-based CO₂ emissions model designed for rental vehicles

Yousfi

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Impact of digitalization of public transport and city mobility on traffic structure and on-road emissions

Netov

Lomev

2022

2022 6th European Conference on Electrical Engineering &Amp; Computer Science (ELECS)

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Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation

Eren,

Cesur

2024

Sakarya University Journal of Computer and Information Sciences

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In recent years, increasing concerns about vehicle emissions' environmental and public health impacts have led to the desire to use eco-friendly fuels as alternatives to traditional fossil fuels. Biofuels, hydrogen, and electric power offer lower greenhouse gas emissions and improved air quality, resulting in their development and adoption globally. Predicting vehicle emissions using these fuels is crucial for assessing their environmental benefits. This study proposes using artificial neural networks (ANN), a machine learning technique, to accurately predict vehicle emissions associated with eco-friendly fuels across different compositions and engine speeds. The ANN model has a strong correlation between predicted and observed emissions values, indicating the effectiveness of its model. The research underscores the importance of adopting innovative approaches to address environmental challenges and promote sustainable transportation solutions. This study contributes to reducing the adverse effects of vehicle emissions on air quality and public health by assisting policymakers, car manufacturers, and city planners in making effective decisions. It promotes environmental sustainability by providing valuable insights into vehicle emissions prediction and guiding the development of eco-friendly fuels for a more efficient transportation system.

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Predicting CO2 Emissions by Vehicles Using Machine Learning

Cited by 4 publications

References 17 publications

ML-ERV: A machine learning-based CO₂ emissions model designed for rental vehicles

ML-ERV: A machine learning-based CO₂ emissions model designed for rental vehicles

Impact of digitalization of public transport and city mobility on traffic structure and on-road emissions

Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation

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Predicting CO2 Emissions by Vehicles Using Machine Learning

Cited by 4 publications

References 17 publications

ML-ERV: A machine learning-based CO2 emissions model designed for rental vehicles

ML-ERV: A machine learning-based CO2 emissions model designed for rental vehicles

Impact of digitalization of public transport and city mobility on traffic structure and on-road emissions

Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation

Contact Info

Product

Resources

About

ML-ERV: A machine learning-based CO₂ emissions model designed for rental vehicles

ML-ERV: A machine learning-based CO₂ emissions model designed for rental vehicles