Safe Movement and Accident Prevention for the Railways

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.