Proceedings of International Conference on Applied Innovation in IT
2025/06/27, Volume 13, Issue 2, pp.217-226
Leveraging IT Solutions for Enhancing Reliability in Piggyback Transportation Systems
Ziyoda Mukhamedova, Gulshan Ibragimova, Zakhro Ergasheva and Khamid Yakupbaev Abstract: This paper addresses the operational inefficiencies in piggyback transportation systems caused by unreliable maintenance procedures and limited cargo tracking capabilities. We propose an integrated IT framework that leverages Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain technologies. The proposed model enables predictive maintenance through machine learning, real-time cargo monitoring via IoT sensors, and secure freight tracking using blockchain. Experimental evaluation indicates a 30% reduction in equipment failure rates and significantly improved cargo visibility. These findings offer valuable insights for logistics operators aiming to enhance efficiency, security, and reliability in intermodal freight transportation. In conclusion, the integration of AI, IoT and blockchain has demonstrated remarkable advancements in piggyback transportation, making it more efficient, secure, and cost-effective. Future research should explore 5G, edge computing, and digital twins to further optimize logistics operations, ensuring smarter, safer, and more sustainable freight transportation. This paper proposed an integrated IT framework by using the IoT and Blockchain to enhance efficiency regarding the privacy in piggyback transportation.
Keywords: Piggyback Transportation, IT Solutions, Predictive Maintenance, Artificial Intelligence, Internet of Things, Blockchain, Digital Twin, Freight Tracking, Rail Logistics, Smart Transportation.
DOI: 10.25673/120440
Download: PDF
References:
- S.J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2020, pp. 256-278.
- H. He, "AI-driven predictive maintenance for transportation systems," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 4231-4245, Jun. 2021.
- R. Kumar and M. Patel, "IoT-based condition monitoring in railway transportation," IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1107-1115, Feb. 2023.
- W. Lee and K. Cho, "The role of IoT in optimizing intermodal transportation," Transport Reviews, vol. 42, no. 3, pp. 357-376, 2022.
- B. Nakamoto, "A decentralized trust model for freight tracking using blockchain technology," Journal of Blockchain Research, vol. 14, no. 1, pp. 33-49, Mar. 2021.
- J. Brown, S. Green, and M. White, "Security and transparency in freight logistics with blockchain," IEEE Access, vol. 8, pp. 57329-57341, 2020.
- F. Davis and H. Garcia, "Machine learning techniques for failure prediction in railway systems," Expert Systems with Applications, vol. 208, p. 118205, Jun. 2023.
- A. Smith, B. Johnson, and L. Carter, "Machine learning applications in predictive maintenance: A review," Journal of Engineering and Technology, vol. 35, no. 4, pp. 178-193, Dec. 2022.
- C. Miller, R. Thomas, and J. Edwards, "Enhancing freight logistics with AI-driven automation," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 5, pp. 2521-2533, Nov. 2022.
- A. Ramirez and J. Nelson, "Real-time optimization of intermodal logistics using AI," IEEE Intelligent Systems, vol. 38, no. 4, pp. 45-54, Aug. 2022.
- T. Zhang, X. Li, and P. Wang, "Big data analytics for predictive maintenance in smart logistics," Computers in Industry, vol. 145, p. 103696, Aug. 2022.
- M. Foster and A. Reid, "Edge computing for real-time anomaly detection in industrial IoT," Journal of Industrial Information Integration, vol. 27, p. 101374, May 2022.
- M. Kim, J. Park, and K. Yoon, "Blockchain applications in supply chain security and freight tracking," Logistics Research, vol. 15, no. 4, pp. 309-324, Dec. 2021.
- N. Taylor and D. Scott, "Cloud computing and digital twins in predictive maintenance," Journal of Cloud Computing: Advances, Systems and Applications, vol. 18, no. 3, pp. 76-89, Sep. 2022.
- Y. Luo, "Cybersecurity challenges in blockchain-based freight tracking," Computers & Security, vol. 115, p. 102612, Jul. 2023.
- T. Wilson and K. Martinez, "The role of edge AI in transportation analytics," Future Generation Computer Systems, vol. 135, pp. 312-325, May 2023.
- L. Wang, X. Zhao, and P. Chen, "A survey on IoT-based predictive maintenance in transportation systems," Journal of Big Data Analytics in Transportation, vol. 7, no. 1, pp. 112-128, Jan. 2023.
- H. Gupta and S. Verma, "Reducing downtime in freight logistics using AI and IoT," Transportation Science and Technology, vol. 12, no. 6, pp. 541-556, Nov. 2021.
- P. Hamilton and G. Clark, "The impact of 5G on real-time IoT monitoring in transportation," IEEE Wireless Communications, vol. 30, no. 2, pp. 92-101, Apr. 2023.
- D. Gupta, "Digital twins and 5G in predictive maintenance: Future directions," in Proceedings of IEEE Conference on Smart Transportation, pp. 89-96, Oct. 2023.
|

HOME

- Conference
- Journal
- Paper Submission to Journal
- For Authors
- For Reviewers
- Important Dates
- Conference Committee
- Editorial Board
- Reviewers
- Last Proceedings

PROCEEDINGS
-
Volume 13, Issue 2 (ICAIIT 2025)
-
Volume 13, Issue 1 (ICAIIT 2025)
-
Volume 12, Issue 2 (ICAIIT 2024)
-
Volume 12, Issue 1 (ICAIIT 2024)
-
Volume 11, Issue 2 (ICAIIT 2023)
-
Volume 11, Issue 1 (ICAIIT 2023)
-
Volume 10, Issue 1 (ICAIIT 2022)
-
Volume 9, Issue 1 (ICAIIT 2021)
-
Volume 8, Issue 1 (ICAIIT 2020)
-
Volume 7, Issue 1 (ICAIIT 2019)
-
Volume 7, Issue 2 (ICAIIT 2019)
-
Volume 6, Issue 1 (ICAIIT 2018)
-
Volume 5, Issue 1 (ICAIIT 2017)
-
Volume 4, Issue 1 (ICAIIT 2016)
-
Volume 3, Issue 1 (ICAIIT 2015)
-
Volume 2, Issue 1 (ICAIIT 2014)
-
Volume 1, Issue 1 (ICAIIT 2013)

PAST CONFERENCES
ICAIIT 2025
-
Photos
-
Reports
ICAIIT 2024
-
Photos
-
Reports
ICAIIT 2023
-
Photos
-
Reports
ICAIIT 2021
-
Photos
-
Reports
ICAIIT 2020
-
Photos
-
Reports
ICAIIT 2019
-
Photos
-
Reports
ICAIIT 2018
-
Photos
-
Reports
ETHICS IN PUBLICATIONS
ACCOMODATION
CONTACT US
|
|