Open Access Open Access  Restricted Access Subscription Access

Accident Prevention of Train & Track Fault

Dr. Rajashree Thosar, Dr. Devidas Thosar, Adarsh Tambale, Piyush Tapre, Shraddha Satpute

Abstract


This project focuses on enhancing railway safety by developing an automated system to prevent accidents caused by unmonitored crossings and undetected track cracks. The system uses infrared (IR) sensors for automated railway gate control and crack detection, with data processed by an Arduino microcontroller. When a crack is detected, GPS and GSM modules relay the location to the control room. The automation of gates and real-time crack monitoring aims to reduce human error and improve safety. Implementing this system on a large scale could significantly minimize railway accidents and enhance transportation efficiency. Train accidents, often resulting from track faults and mechanical failures, pose significant risks to human life, infrastructure, and economic stability. This study explores advanced methods and technologies aimed at preventing train accidents caused by track irregularities, equipment malfunctions, and human error. Emphasis is placed on real-time monitoring systems, predictive maintenance using AI and IoT, and automated inspection techniques such as ultrasonic testing and drone surveillance. The integration of smart sensors and data analytics enables early detection of track anomalies and vehicle issues, significantly reducing the likelihood of derailments and collisions. Furthermore, this paper discusses the role of signaling systems, communication protocols, and railway safety regulations in enhancing accident prevention strategies. The objective is to develop a proactive and intelligent railway management system that ensures safer and more efficient rail transportation.

 


Full Text:

PDF

References


A. Kumar, R. Singh, and M. Sharma, "AI-Based Predictive Maintenance System for Railway Track Fault Detection," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 1452-1463, Apr. 2022.

J. Brown, S. Patel, and L. Chang, "Wireless Sensor Networks for Real-Time Track Fault Detection in Railway Systems," IEEE Sensors Journal, vol. 21, no. 9, pp. 8004-8012, May 2019.

Y. Zhang, X. Liu, and Q. Wang, "Data-Driven Accident Prediction for Railway Systems Using Predictive Modeling Techniques," IEEE Access, vol. 30, pp. 104312-104325, Sept. 2022.

Devidas S Thosar, Rajashree R Shinde, Prashant J Gadakh, Pratibha V Kashid, Secure kNN Query Processing in Entrusted Cloud Environments , Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, Issue I , Vol 2 (2016).

D. S. Thosar, R. D. Thosar, P. B. Dhamdhere, S. B. Ananda, U. D. Butkar and D. S. Dabhade, "Optical Flow Self-Teaching in Multi-Frame with Full-Image Warping via Unsupervised Recurrent All-Pairs Field Transform," 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI), Wardha, India, 2024, pp. 1-4, doi: 10.1109/IDICAIEI61867.2024.10842718.

M. Tiwari and A. Prasad, "Automated Railway Crossing Systems Using IoT for Improved Safety," IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4600-4610, June 2021.

S. Gupta, P. Roy, and D. K. Das, "A Comprehensive Review on Railway Track Fault Detection Techniques," IEEE Access, vol. 28, pp. 99520-99538, Nov. 2020.

L. Feng and H. Li, "Machine Learning-Based Track Monitoring System for Railway Safety," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 3, pp. 1135-1146, Mar. 2021.

R. K. Singh and S. Verma, "Smart Railway Infrastructure: IoT-Based Accident Prevention Systems," IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 3556-3565, May 2021.

D. S. Thosar and M. Singh, "A Review on Advanced Graphical Authentication to Resist Shoulder Surfing Attack," 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India, 2018, pp. 1-3, doi: 10.1109/ICACAT.2018.8933699.


Refbacks

  • There are currently no refbacks.