Enhancing Railway Accident Prevention Using Deep Learning, Machine Learning, And GPS Tracking: A Historical And Knowledge-Based Analysis

Abstract: This research extends the foundational study “Enhancing Railway Accident Prevention Using Deep Learning, Machine Learning, and GPS Tracking” (Vikas Chandra Giri & Parineeta Jha, 2025). The present work introduces an adaptive framework that integrates Deep Learning (DL), Internet of Things (IoT) sensor networks, and real-time GPS analytics to advance predictive railway safety. With results Sets in these volume.The architecture enhances detection accuracy, reduces response latency, and improves contextual awareness using hybrid CNN–LSTM–Random Forest fusion models with a multi-layer IoT sensing infrastructure. The model achieves 97.8% accuracy and maintains average GPS latency under 4 seconds, marking a significant improvement over earlier implementations.Through continuous learning and edge-cloud synchronization, the system advances toward fully autonomous accident prevention, predictive maintenance, and real-time operational intelligence across railway networks.

Keywords: Deep Learning · IoT · LSTM · GPS Tracking · Predictive Analytics · Accident Prevention. ML, MQTT