AI-Enhanced Systems for Vehicle Fleet Telematics

Authors

  • Dr. Juan Gómez-Olmos Associate Professor of Computer Science, University of Jaén, Spain Author

Keywords:

Vehicle, Fleet, Telematics, AI

Abstract

Vehicle fleet telematics optimizes fleet utilization by integrating telecommunications with monitoring systems. It is the union of two words: telecommunications and informatics. This helps to manage and monitor the installed systems on the newly managed vehicles effectively and efficiently, as these systems can be in constant communication with the control center or data center, and the data can be continuously monitored by the telematics applications. Efficient management leads to considerable cost reductions.

Telematics is a major engineering technological improvement and will enhance the safety, security, maintenance, and operation of modern transport systems, regardless of the origin, destination, and mode of travel. The focus in the last decade has been on incorporating vehicle fleet monitoring systems for timely decisions on incident detection, status monitoring, and location for safety or emergency tracking/communication, and, in some applications, collection of sensor data that includes automatic counting of passengers on public transit vehicles. Telematics is based on the following three components: GPS tracking devices, navigation software, and communication systems such as shortwave radio and satellite systems for communicating and transmitting to the control center. Telematics is experiencing rapid growth in order to collect, transmit, and interpret data from vehicle performance, location, speed, time, and diagnosis of possible issues or the after-execution of planned services. Modern operations find that immediate data analysis must be prioritized, or the position of the vehicle must be processed and updated in real time. Its services can be of enormous value to fleet operators who manage large and complex fleets. The service is not only to provide the right freight delivery on time and at a reduced cost, but also to rationalize the choice of carriers, to show, and allow tracking on the ship.

Downloads

Download data is not yet available.

References

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Singh, Jaswinder. "Deepfakes: The Threat to Data Authenticity and Public Trust in the Age of AI-Driven Manipulation of Visual and Audio Content." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 428-467.

Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.

S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021

Tamanampudi, Venkata Mohit. "AI and DevOps: Enhancing Pipeline Automation with Deep Learning Models for Predictive Resource Scaling and Fault Tolerance." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 38-77.

Downloads

Published

21-11-2022

How to Cite

[1]
D. J. Gómez-Olmos, “AI-Enhanced Systems for Vehicle Fleet Telematics”, Distrib Learn Broad Appl Sci Res, vol. 8, pp. 190–206, Nov. 2022, Accessed: Nov. 15, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/174

Similar Articles

51-60 of 99

You may also start an advanced similarity search for this article.