Artificial intelligence applied to truck emissions reduction: A novel emissions calculation model

Abstract

Meeting climate targets requires robust carbon reduction strategies, particularly in the context of road transportation. This study presents a predictive model that integrates CO₂ emissions and operational costs for heavy-duty truck loads using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) optimization. The model identifies the optimal vehicle driving profile by balancing environmental sustainability and economic efficiency. A strong correlation between vehicle weight and speed and CO₂ emissions was found, with the optimal weight and speed parameters being 49.67 tons and 31.00–36.61 km/h, respectively. The proposed model was tested across five scenarios, with the total cost per kilometer and emissions scenario yielding the best performance. The results demonstrate significant cost reductions, ranging from 31.4 % to 40.5 %, which not only reflect operational but also environmental cost savings. By optimizing driving parameters, fleet managers and decision makers can implement strategies to reduce operational and environmental costs, promoting sustainable transportation practices.