International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 21-27, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
M.Manivannan
G.Hema
P.Manasa
D.Bindhu
D.Mohith
M.Divakar
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Railway transportation systems operate in complex and safety-critical environments where accidents may occur due to factors such as human-behavioral risks, trespassing, wildlife intrusion, overcrowding, and operational monitoring limitations. Traditional rule-based surveillance and manual observation methods often fail to identify subtle anomalies or emerging accident-risk situations in real time. This study proposes an intelligent safety-enhancement framework for railway-station and track-side environments, focusing on proactive anomaly detection, risk-event analysis, and safety-aware decision support. The framework integrates multi-source monitoring data, behavioral-pattern assessment, and analytical evaluation to identify unsafe events such as unauthorized track access, congestion-risk formation, and hazardous movement near critical zones. Experimental evaluation is conducted using standard performance metrics including accuracy, precision, recall, F1-score, and error rate to assess detection reliability and operational relevance. The findings demonstrate that intelligent analytical monitoring enhances situational awareness, supports early-stage accident prevention, and strengthens overall railway-safety management capabilities.
Keywords: Railway Safety, Accident Prevention, Intelligent Monitoring, Anomaly Detection, Human Factors.
References:
- S. R. M. Sudhanan, A. K. Rahuman, and S. M. M. Roomi, “Using Mask R-CNN and distance transform to detect elephants on railway tracks for collision prevention,” Measurement, vol. 248, p. 116951, Feb. 2025, doi: 10.1016/j.measurement.2025.116951.
- G. M. Havârneanu, J.-M. Burkhardt, and F. Paran, “A systematic review of the literature on safety measures to prevent railway suicides and trespassing accidents,” Accident Analysis & Prevention, vol. 81, pp. 30–50, May 2015, doi: 10.1016/j.aap.2015.04.012.
- V. Turkova, A. Arkhipova, G. Yusupova, and R. Ardashev, “Economic and legal aspects of violations of traffic safety rules on railway transport in Russia,” Transportation Research Procedia, vol. 63, pp. 472–478, Jan. 2022, doi: 10.1016/j.trpro.2022.06.037.
- C. Li, T. Tang, M. M. Chatzimichailidou, G. T. Jun, and P. Waterson, “A hybrid human and organisational analysis method for railway accidents based on STAMP-HFACS and human information processing,” Applied Ergonomics, vol. 79, pp. 122–142, Feb. 2019, doi: 10.1016/j.apergo.2018.12.011.
- Z. Yu, H. Wang, and F. Chen, “Security of railway control systems: A survey, research issues and challenges,” High-speed Railway, vol. 1, no. 1, pp. 6–17, Dec. 2022, doi: 10.1016/j.hspr.2022.12.001.
- A. W. Evans, “The economics of railway safety,” Research in Transportation Economics, vol. 43, no. 1, pp. 137–147, Jan. 2013, doi: 10.1016/j.retrec.2012.12.003.
- P. Singh, Z. Elmi, V. K. Meriga, J. Pasha, and M. A. Dulebenets, “Internet of Things for sustainable railway transportation: Past, present, and future,” Cleaner Logistics and Supply Chain, vol. 4, p. 100065, Jun. 2022, doi: 10.1016/j.clscn.2022.100065.
- Md. A. K. Rifat, A. Kabir, and A. Huq, “An explainable machine learning approach to traffic accident fatality prediction,” Procedia Computer Science, vol. 246, pp. 1905–1914, Jan. 2024, doi: 10.1016/j.procs.2024.09.704.
- M. Li, F. Yan, R. Niu, and N. Xiang, “Identification of causal scenarios and application of leading indicators in the interconnection mode of urban rail transit based on STPA,” Journal of Rail Transport Planning & Management, vol. 17, p. 100238, Jan. 2021, doi: 10.1016/j.jrtpm.2021.100238.
- J. Hu et al., “Ensuring combination of strength, ductility and toughness in medium-manganese steel through optimization of nano-scale metastable austenite,” Materials Characterization, vol. 136, pp. 20–28, Dec. 2017, doi: 10.1016/j.matchar.2017.11.058.
- S. Kabir and Y. Papadopoulos, “Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review,” Safety Science, vol. 115, pp. 154–175, Feb. 2019, doi: 10.1016/j.ssci.2019.02.009.
- T. Kongsvik, P. Almklov, and J. Fenstad, “Organisational safety indicators: Some conceptual considerations and a supplementary qualitative approach,” Safety Science, vol. 48, no. 10, pp. 1402–1411, Jul. 2010, doi: 10.1016/j.ssci.2010.05.016.