Advanced IoT Solution for Early Detection of Railway Hazards

Abstract

The expanded development in the railroad area has brought about an expansion in the train activity thickness over the world. This has brought about the expansion in the quantity of mischances including trains. In this paper, the proposed framework which deals with the problems related to the railways this system monitors the track, platforms and trains regularly This framework makes utilization of Ultrasonic sensors IR sensors, fire sensor, GSM, GPS and other inserted frameworks Rail mischances have been expanded because of the surge streaming over the Railway tracks. We are proposing a system which is capableofobjectdetection,firedetection,crackdetectionandplatformbarriersystemtoavoidaccidentsonplatformsand weare also automating the rail crossing gates.

Introduction

The project aims to enhance railway safety by preventing accidents on tracks and platforms through continuous monitoring and automation. It uses multiple sensors—ultrasonic for object detection, infrared (IR) for crack detection, fire sensors for fire detection, and automated gate control via sensors—to detect hazards early. When an obstacle or crack is detected, the system slows down the train using air brakes, alerts nearby people via alarms, and notifies officials with GPS location data.

The system integrates technologies such as GSM for wireless communication, GPS for real-time tracking, Bluetooth/Android apps for device interaction, and servo motors for controlling gates and alarms. Data from sensors is transmitted to cloud platforms and analyzed with machine learning to predict hazards and reduce accidents.

Results show high accuracy in crack and fire detection, fast alert response, and improved railway operational efficiency. The project significantly reduces accident risks and maintenance needs, demonstrating scalability and adaptability for wider use.

Future plans include integration with existing railway management, expansion to more hazard types, predictive maintenance, drone inspections, AI-powered decision support, and nationwide deployment. The system promises to improve railway safety, passenger experience, and environmental impact while fostering industry collaborations and creating jobs.

Conclusion

The Advanced IoT Solution for Early Detection of Railway Hazards has a promising future scope, with numerous opportunities for growth and innovation. In the short term (6- 12 months), the project aims to integrate with existing railway management systems, expand to other hazard detection parameters, develop advanced data analytics, and explore 5G connectivity.Mid-termgoals(1-2years)includedeploymentin multiple railway stations and tracks, integration with railway signaling systems, predictive maintenance scheduling, and automated drone inspection. Long-term objectives (2-5 years) encompass nationwide deployment, standardization, integration with other transportation modes, and development of AI-powered decision support systems. Potential research directions include investigating computer vision for hazard detection, sensor fusion techniques, blockchain for secure data storage, and autonomous systems for maintenance. Industry collaborations with railway authorities, sensor manufacturers, and software providers will facilitate seamless integration and adoption. Business models will focus on subscription-based monitoringservices,sensorsales,dataanalyticsconsulting,and intellectual property licensing. The project\'s social impact will be significant, improving railway safety, enhancing passenger experience, reducing environmental impact, and creating jobs in the railway and IoT industries.

References

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Copyright

Copyright © 2025 V. Sakthivel, Naveen V S, Pavithra S, Rajkumar P, Rubika N. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.