A Survey on Onboard Computing Network based Railway Gate Monitoring System

Abstract

The Railway Gate Monitoring System using onboard computing techniques is designed to enhance safety and automation at railway level crossings. It employs onboard units installed on trains, which are equipped with GPS, RFID, and wireless communication modules to continuously track the train\'s location, speed, and direction. This real- time data is transmitted to a microcontroller-based gate control unit situated near the crossing. As the train approaches, the gate automatically closes, remains shut while the train passes, and opens only after it has safely crossed. The system minimizes human intervention, reducing the risk of human error. Onboard computing enables fast and accurate decision-making based on sensor data and predefined logic. Additional sensors like IR or ultrasonic detectors can be used to identify obstacles at the crossing. CCTV integration can further monitor and record gate activity for safety audits. This project is cost-effective, scalable, and ideal for remote or unmanned crossings. Overall, it significantly improves safety and efficiency in railway transportation systems.

Introduction

Railway level crossings are high-risk areas prone to accidents due to delayed responses, human error, and lack of real-time monitoring. To address these issues, modern railway gate monitoring systems integrate onboard computing, embedded controllers, sensor networks, and wireless communication technologies.

Key technologies used include:

• FPGAs and microcontrollers for real-time processing.

• Sensors (IR, ultrasonic, thermal) for train detection and obstacle monitoring.

• Communication modules (IR, GSM, LoRa, GPS) for data transmission and alerts.

• Autonomous control of gates based on train movement without human input.

These systems enhance safety, minimize human error, and improve efficiency at level crossings, both in urban and remote areas.

2. Related Work Summary

A review of 17 research papers reveals a range of approaches and innovations:

• Sensor-based automation using IR, PIR, ultrasonic, and thermal sensors.

• IoT and Arduino platforms for real-time control and low-cost deployment.

• GPS-based systems to eliminate roadside hardware and reduce maintenance.

• Machine learning and computer vision for advanced obstacle detection.

• Communication technologies such as LoRa, GSM, Zigbee for remote monitoring.

Common benefits:

• Increased automation.

• Reduced human error.

• Improved safety and real-time alerts.

Common challenges:

• Dependence on sensor accuracy and environmental robustness.

• Network reliability issues.

• Limited scalability in harsh or varied conditions.

Future suggestions include AI/ML integration, advanced dashboards, and more robust sensing systems.

3. Model Comparison Highlights

The comparison of proposed systems focuses on:

• Detection technologies (e.g., sensors vs. GPS vs. computer vision).

• Communication methods (e.g., GSM, LoRa, D2D).

• Gate operation techniques (manual, semi-automated, fully automated).

• Response time and system complexity.

Findings:

• IoT-based systems offer flexibility and cost-effectiveness but may suffer from environmental and signal-related issues.

• GPS-based models reduce hardware needs but depend heavily on signal reliability.

• AI-integrated systems show promise in predictive safety but face computational limitations.

Conclusion

The Railway Gate Automation System using onboard computing networks provides an effective and intelligent solution to enhance safety and efficiency at railway level crossings. By utilizing onboard units equipped with GPS, RFID, and wireless communication, the system enables real-time tracking of train movement and automates gate operations without human intervention. This significantly reduces the chances of accidents caused by delays or manual errors. Additional features like IR and ultrasonic sensors help detect obstacles on the track, while CCTV integration supports monitoring and auditing. Cost-effective, scalable, and reliable, the system is well-suited for deployment in remote or unmanned locations, contributing to a safer and smarter railway infrastructure.

References

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Copyright

Copyright © 2025 Sanketh U Gujjar, Poornima K M. 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.