AI-Enabled Real-Time Vehicle Tracking Model with Mobile Apps

This study presents an AI-based real-time vehicle tracking model integrated with a mobile application for institutional use. The system is designed to improve the efficiency, accuracy, and reliability of vehicle monitoring and management by utilizing multiple sensors, high-precision GPS positioning, and advanced artificial intelligence algorithms. The core components of the system include a mobile application for real-time vehicle location display, a back-end server for data processing and storage, GPS devices installed in vehicles, and AI algorithms for data analysis. The methodology section details the implementation of these components and their integration. The performance of the system was evaluated through extensive experiments, which showed that the combination of the three core modules significantly improved the tracking accuracy. Module 1 alone achieved an accuracy of about 90%, which increased to about 95% with the addition of Module 2, and further to nearly 99% with the integration of Module 3. These results demonstrate the robustness of the system and its potential for application in various sectors, including logistics, public transportation, government, and the commercial vehicle management.