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