AI-Based Smart Bus Live Tracking System Using IoT and Machine Learning: A Survey of Real-Time ETA Prediction and Transportation Analytics Approaches

Abstract: Public transportation systems in urban areas suffer from persistent inefficiencies rooted in static schedule management, absent live vehicle visibility, and reactive fleet operations. Passengers endure uncertainty regarding bus arrival times, leading to excessive stop-level waiting and reduced confidence in public transit. This paper surveys existing research on IoT-enabled bus tracking, AI-based estimated time of arrival (ETA) prediction, GPS-based fleet monitoring, and machine learning applied to transportation analytics. Six representative studies from 2022–2026 are analyzed and compared across methodology, hardware configuration, AI algorithm, cloud platform, and key limitations. Based on this survey, we propose a comprehensive AI-Based Smart Bus Live Tracking System integrating ESP32 microcontrollers, GPS modules, cloud-synchronized databases, machine learning-based ETA prediction, and a cross-platform Flutter passenger application. The proposed system addresses critical gaps in existing approaches by combining real-time GPS tracking, multi-parameter AI prediction, and a centralized analytics dashboard within a single deployable platform.

Keywords: IoT, GPS, ESP32, ETA Prediction, Machine Learning, Smart Transportation, Real-Time Tracking, Cloud Computing, Flutter, Transportation Analytics, LSTM, Random Forest, Firebase, Smart City