Abstract
Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.
Official URL
More Information
Divisions: | Leeds Business School School of Built Environment, Engineering and Computing |
---|---|
Identification Number: | https://doi.org/10.20517/ces.2024.35 |
Status: | Published |
Refereed: | Yes |
Publisher: | OAE Publishing Inc. |
Additional Information: | © The Author(s) 2024 |
SWORD Depositor: | Symplectic |
Depositing User (symplectic) | Deposited by Mann, Elizabeth |
Date Deposited: | 24 Oct 2024 13:25 |
Last Modified: | 24 Oct 2024 15:47 |
Item Type: | Article |
Export Citation
Explore Further
Read more research from the author(s):
- H Fatorachian ORCID: 0000-0002-2569-7882
- H Kazemi ORCID: 0000-0001-5982-2605