The dynamic behavior of factors such as product demand, travel distance, fuel price, and vehicle characteristics influencing the transportation system makes it challenging to develop a sustainable transportation model for predicting the system’s adaptability in the future. Hence, the present study proposes a novel quantitative model simulated using the system dynamics approach to visualize the influence of the considered dynamic factors of the sustainability objectives. Ensemble learning is employed across four machine learning algorithms to enhance the accuracy and reliability of predictions for the dynamic behavior of the model, considering a specific case scenario. Sobol sensitivity analysis is performed to visualize the relationships between input and output variables. Five quantitative scenario analyses have been conducted to explore the system’s future state under uncertainty. The results indicate that fuel price, vehicle speed, and product demand are crucial input parameters, while transportation costs exhibit substantial fluctuations as an output in the sustainable transportation problem.
Paul et al. (Mon,) studied this question.