Artificial Intelligence in Road Transportation: Opportunities and Challenges for Carbon Emission Reduction

This study discussed the nexus between Artificial Intelligence (AI) and carbon emissions in road transportation. It highlighted AI’s potential as a promising tool for carbon footprint management in this sector. The study considered different carbon emission reduction strategies, such as fuel consumption optimization, speed management, traffic control systems, and vehicle fleet policies. This study acknowledged AI’s emerging roles in providing real-time information, reducing implementation costs, and minimizing human bias in road traffic control and emission monitoring. In this study, it was observed that different AI algorithms, such as artificial neural networks and support vector machines have been used for traffic management and CO2 emission prediction. This study reported the growing global market for AI technologies in transportation due to its potential to analyse historical data and optimize various aspects of transportation systems. However, several challenges affect AI adoption in this sector. This includes limited information on how to manage data quality and availability in the industry, issues of system failures and cyber-attacks, lack of trust, and fear of job displacement that are associated with AI adoption in the transportation sector. The study further provided insights into the complexity of integrating AI into this sector's operations. Lastly, this study acknowledged the need for continued research on AI in the transport sector, while acknowledging the need for more investment in AI and robust policy development in order to sustain an efficient transportation system.