International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Public bus transportation continues to be the primary mode of travel for millions of commuters in major Indian metropolitan cities. Although several digital bus tracking applications are available, most existing systems suffer from inaccurate Estimated Time of Arrival (ETA) predictions, limited awareness of passenger crowd levels, slow data update rates, and a lack of intelligent predictive features. These shortcomings often lead to longer waiting times, overcrowded buses, and an overall unsatisfactory commuting experience.

To address these challenges, this paper proposes an AI-driven predictive platform for bus mobility and real-time human flow analysis. The platform integrates live GPS tracking with advanced machine learning models to deliver accurate and reliable transit information. Long Short-Term Memory (LSTM) networks are used to predict bus arrival times, while Random Forest algorithms estimate passenger crowd levels. In addition, the system incorporates real-time WebSocket communication, weather-aware routing, and user travel pattern learning through K-Means clustering. Developed as a Progressive Web Application (PWA), the platform supports multi-city scalability, offline access, and real-time notifications. Experimental results demonstrate an ETA prediction accuracy of up to 85% and crowd classification accuracy of 78%, significantly outperforming existing public transport applications. Overall, the proposed solution improves commuter decision-making, reduces travel uncertainty, and contributes to the development of smarter and more efficient urban mobility systems.

Keywords: Bus Tracking, ETA Prediction, Human Flow Analysis, LSTM, Random Forest, Intelligent Transportation Systems.

Downloads: | DOI: 10.17148/IJARCCE.2026.151153