The Genetic Algorithm-Artificial Neural Networks Integration in the Optimization: An Application for Transportation Systems

The transportation sector

stands as one of the broadest and most significant industries worldwide. Today,

one of the key environmental consequences of the transportation sector is the

increase in carbon dioxide (CO2) emissions. This research focuses on CO2

emissions, specifically from road transportation. The primary objectives of the

study are to investigate how yearly fluctuations on highways impact CO2

emissions. The study utilizes genetic algorithm-based artificial neural

networks (GA-ANN) to design a transportation optimization model. Artificial

neural networks are employed to estimate the proportion of CO2 emissions from

roadways in the total transportation-related CO2 emissions. The article

proposes an optimization method using genetic algorithms (GA) to optimize the parameters

of the artificial neural networks. The development of the ANN model was carried out out in the MATLAB program. The constructed GA-ANN model was compared to a

variety of other machine learning (ML) models, such as linear regression (LR), support

vector machine (SVM), random forest (RF), and artificial neural network (ANN).