Development of Bus Tracking System Using Radio Frequency Identification (RFID) and Artificial Intelligence (AI) Implementation

In recent years, universities implemented shuttle bus services to facilitate campus transportation for students without personal vehicles. However, students often encounter challenges in accurately estimating bus arrival times, leading to extended wait times or missed buses. This project aims to develop a cost-effective prototype bus tracking system by implementing Radio Frequency Identification (RFID) technology. The developed system provides students with real-time bus location updates and estimates the time of arrival (ETA) at their designated stops. ETA predictions are generated using a machine learning approach, specifically a linear regression model. The system utilizes ultra-high frequency RFID tags, a YPD-R200 RFID reader module board with reading range of up to 5 meters, and a WeMos D1 microcontroller for uploading data to the Blynk cloud platform. Users can access real-time bus information and status updates through the Blynk IoT application. The prototype had been successfully built and tested. This research surely can improve the students’ experience utilizing the bus services by enabling the students to track the bus and giving precise arrival time predictions for the bus timetables.