Publication Type : Conference Paper
Publisher : IEEE
Source : 2025 5th International Conference on Expert Clouds and Applications (ICOECA)
Url : https://doi.org/10.1109/icoeca66273.2025.00057
Campus : Chennai
School : School of Engineering
Year : 2025
Abstract : Accurately predicting transportation arrival times in dynamic traffic conditions remains a significant challenge. To address this issue, this paper proposes a scalable and cost-effective Real-Time Vehicle Tracking System (RTVTS) designed specifically for educational transport services. The RTVTS integrates Raspberry Pi, NEO-6M GPS, and GSM 800C modules to capture precise location data, which is transmitted to a central server via Wi-Fi. This data is made accessible through an interactive web-based platform, enabling real-time vehicle tracking and route monitoring. When a vehicle enters a geofenced area, the Raspberry Pi processes the location data, and the GSM module automatically sends a pre-arrival SMS to registered mobile numbers at designated stops. In addition to real-time tracking, the RTVTS incorporates features such as fuel status monitoring and speed tracking to enhance safety and operational efficiency. A dedicated fleet mobile app further extends the system's capabilities by providing real-time vehicle monitoring, user access management, and maintenance reminders. Notably, the proposed system ensures reliable pre-arrival notifications even without an internet connection, leveraging the GSM module to send messages directly to registered mobile numbers. With its cost-effective hardware components and user-friendly interfaces, the RTVTS offers a reliable and affordable solution for improving service quality, ensuring timely arrivals, and enhancing the overall efficiency of educational transportation systems.
Cite this Research Publication : Barath E, Hirthicik J S Suriyah, Devanathan B, Lakshmanan S A, IoT Enabled Real-Time Vehicle Tracking and Alert System for Educational Transport Service, 2025 5th International Conference on Expert Clouds and Applications (ICOECA), IEEE, 2025, https://doi.org/10.1109/icoeca66273.2025.00057