Automated Inspection System with GPS and Deep Learning in Urban Rail Safety and Efficiency

Abstract

Paper focuses on a novel automated rail inspection system, incorporating advanced technologies such as GPS for precise location tracking and GSM/GPRS modules for efficient data transmission. The system uses deep learning to analyze vibration data collected during train transitions, enabling predictive maintenance and enhancing rail safety within urban smart city frameworks. This system represents a significant step forward in intelligent transportation systems by automating and improving the efficiency and accuracy of rail inspections. The use of deep learning for data analysis underscores the potential of AI in infrastructure maintenance, potentially setting a new standard for rail safety protocols. The paper discusses the technical design of the sensor node, the integration of GPS and GSM/GPRS modules, the application of deep learning algorithms, and the analysis of the system's performance through testing and validation. The implications of such a system for smart city infrastructure and urban planning, as well as potential future enhancements and applications of the technology.