Assessing AI-based eco-driving solutions for reducing GHG emissions in green transportation systems

The transportation sector in the Kingdom of Saudi Arabia (KSA) is a major contributor to greenhouse gas (GHG) emissions, driven by the country's heavy reliance on oil and fossil fuels. Transitioning to a green and sustainable transport system is critical for reducing emissions and aligning with Saudi Arabia's Vision 2030 goals of diversifying its economy and promoting environmental sustainability. Thus, this research examined the adoption of a green sustainable transport system to reduce GHG emissions and reduce dependence on fossil fuels for sustainable development in the KSA. The study evaluates various factors and Artificial Intelligence (AI)-based eco-driving solutions to systematically implement green transportation systems. In this study, the Fuzzy Analytical Hierarchy Process (FAHP) method is applied to evaluate the five factors and eighteen sub-factors crucial for developing a green transportation system in the country. Next, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) method is used to prioritize the most significant AI-based eco-driving solutions for the implementation of smart and green transportation in KSA. The findings of the FAHP show that environmental impact (33 %) is the most crucial factor, followed by regulatory compliance (21.3 %) and economic viability (16.9 %). The FTOPSIS indicates that the smart navigation system (CC_i = 0.682) is the most critical AI-based eco-driving solution because this can help reduce GHG emissions and increase the efficiency of traffic regulation in the country. The electric and hybrid vehicle integration (CC_i = 0.585) and carbon footprint tracking systems (CC_i = 0.355) are the next most significant solutions. This study is helpful in reducing GHG emissions, supporting sustainable development, and guiding policymakers toward effective green transport initiatives.