Smart Bus: Crowd-Prediction System

The Smart Bus Crowd Prediction System is an intelligent transportation solution designed to improve passenger convenience and public bus management using Machine Learning (ML), Internet of Things (IoT), Web Technologies, and R Programming. The system collects real-time data from sensors, GPS modules, and passenger inputs to monitor bus occupancy, location, and travel patterns. Machine learning algorithms are used to analyze historical and live data to predict crowd density and bus arrival timings with improved accuracy.A web-based platform is developed to display live bus tracking, predicted crowd levels, and estimated arrival times to passengers and transport authorities. R programming is utilized for data analysis, visualization, and statistical prediction of passenger flow trends. IoT devices enable continuous communication between buses and the central server, ensuring real-time updates and efficient data handling. The proposed system helps passengers avoid overcrowded buses, reduces waiting time, and improves travel planning. It also assists transport authorities in optimizing bus scheduling and resource allocation. By integrating intelligent prediction models with modern web and IoT technologies, the project aims to create a smarter, more efficient, and user-friendly public transportation system.