Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2024
License: CC BY
Data sources: ZENODO
addClaim

Using Artificial Intelligence to Reduce Carbon Emissions from the Saudi Commercial Fleet

Authors: Akram Elentably;

Using Artificial Intelligence to Reduce Carbon Emissions from the Saudi Commercial Fleet

Abstract

The Saudi commercial fleet contributes significantly to the nation's carbon footprint. This paper explores the potential of Artificial Intelligence (AI) to optimize fleet operations and reduce associated emissions. We delve into various AI applications, including predictive maintenance, optimized routing, and intelligent speed control, highlighting their efficacy in minimizing fuel consumption and maximizing operational efficiency. The paper also discusses challenges and opportunities associated with AI implementation in the Saudi context, considering the specific requirements of the local fleet and infrastructure. Ultimately, integrating AI into fleet management systems offers a promising pathway towards achieving sustainability goals and reducing the environmental impact of commercial transport in Saudi Arabia.

Related Organizations
Keywords

Artificial Intelligence, Carbon Emissions, Saudi Arabia, Commercial Fleet, Predictive Maintenance, Optimized Routing, Fuel Efficiency, Sustainability.

Powered by OpenAIRE graph
Found an issue? Give us feedback
Related to Research communities