AI-Driven College Transport System

College buses usually follow fixed schedules and manual coordination, which can cause delays, overcrowding, and inefficiency. This paper presents a smart college transport management system that uses real-time bus tracking through GPS APIs, data-based analysis, and route planning. The system consists of a student mobile application, a driver application, and an admin web dashboard. It predicts bus arrival times, estimates passenger demand, and suggests optimized routes. Student attendance is tracked using an RFIDbased system. Experimental results show reduced waiting time, improved bus utilization, and higher user satisfaction.