The proposed system was evaluated using real-time locomotive signaling data collected from operational railway environments. The dataset consisted of timestamped signaling packets containing locomotive identifiers, station communication events, operational modes, and fault signals. Raw data was continuously ingested into a centralized repository and processed periodically using automated trip-identification algorithms. System and Method for Automated… Experimental evaluation demonstrated that the system can reliably identify journey boundaries from noisy signaling data by applying filtering based on packet type, locomotive mode transitions, and temporal gaps between packets. The preprocessing stage effectively removed redundant and erroneous records generated due to multiple station access attempts or irregular timestamps. Once journeys were identified, the system automatically computed key operational parameters such as journey duration, uptime, downtime, operational availability, and fault occurrences. These metrics were aggregated across locomotive IDs and station identifiers to generate summarized journey reports. The implementation enabled near real-time generation of locomotive performance reports and operational analytics through an API-driven architecture and web-based dashboard interface. Experimental observations indicate that the automated approach significantly reduces manual reporting effort while improving data consistency and traceability in locomotive performance monitoring systems.