Remote Asset Tracking Management Information System

Abstract

Modern freight railways operate at a scale and complexity that make continuous visibility of assets indispensable. Trains cover vast distances, are constantly reconfigured in yards, and move through environments where conventional monitoring tools cannot provide precise or continuous information. In such conditions, the central challenge lies not only in knowing where wagons are located, but in reliably determining which wagons are attached to which locomotive at any given moment. This knowledge is critical for ensuring safe braking performance, efficient use of power, proper cargo delivery, and timely response to operational disruptions.Traditional tracking methods focus primarily on presence detection within fixed segments of track. While effective for basic occupancy monitoring, they fail to capture the dynamic and fine-grained information required for modern operations, particularly when trains are frequently rearranged or move in parallel on complex infrastructure. Emerging reliance on digitalisation and autonomous or remotely controlled freight runs further heightens the demand for real-time, accurate composition data, as human confirmation can no longer serve as a fallback. GPS appears to offer a solution but introduces its own difficulties: noisy positioning, asynchronous reporting, coverage gaps, and missing or delayed updates. Without corrective mechanisms, these issues can obscure the true train composition, leaving operators uncertain whether wagons are properly assigned, detached, or misplaced. This thesis addresses these challenges by developing methods that clean and align incoming data, group assets into coherent trains, and apply predictive logic to bridge gaps when information is incomplete. By doing so, it transforms fragmented GPS signals into a continuous and trustworthy picture of train composition. The outcome is a proof-of-concept system that strengthens safety, enhances logistical reliability, and establishes a digital foundation for the future of efficient, automated freight railway operations.