NAMMA BUS: Intelligent Transportation System Using Deep Learning

Abstract: efficient transportation management plays an important role in educational institutions, where students and faculty members depend on campus buses for daily commuting. Traditional transportation systems often face challenges such as uncertain bus arrival times, traffic delays, lack of real-time tracking, and ineffective communication between drivers and passengers. These issues can lead to longer waiting times, missed buses, and reduced transportation efficiency. To address these limitations, this paper presents NAMMA BUS, an intelligent transportation system designed using Internet of Things (IoT), Global Positioning System (GPS), and Deep Learning-based Regression techniques. The proposed system collects real-time data such as bus location, speed, route information, and traffic conditions using GPSenabled devices installed in buses. The collected data is transmitted to a centralized server through IoT modules for processing and analysis. A Deep Learning Regression model is implemented to predict the Estimated Time of Arrival (ETA) by analyzing historical travel records and live movement patterns. The system follows a three-tier architecture, enabling students to track buses through mobile or web applications, while drivers and administrators manage transportation activities through dedicated dashboards. Experimental results show improved prediction accuracy, reduced passenger waiting time, and enhanced operational transparency, making the system a reliable solution for smart campus transportation.

Keywords: Intelligent transportation systems, deep learning, traffic prediction, vehicle detection, smart traffic management.