Abstract
This thesis explores the framing of the problem space around the current GB rail environment and the integration of autonomous rail, through the practical application of Brian Wilson’s Soft Systems Methodology (SSM), which provides an enterprise viewpoint of the systems and the problem space. This bounding identified key challenges and areas which required further investigation to ascertain whether autonomy is worthwhile to GB rail stakeholders.One of the identified areas for further investigation was capacity, specifically mainline rail capacity, and exploring the potential benefits of autonomy and how to transition to a fully autonomous system. This exploration was achieved through mathematical and simulation-based analysis, the derivation of fundamental diagrams, and the development of a block-based simulator. The analysis examined pure legacy operations, pure autonomous operations, and the mixture of the two.
The simulator’s architecture was developed using the NATO Architecting Framework (NAFv4) and Guidelines for Scenario Development (GSD). Using these frameworks the conceptual models, produced via SSM, were used to develop an executable architecture for the simulator in the form of logistic and resource functionality models and diagrams.
From the analysis of mainline capacity it was identified that capacity could potentially double with pure autonomous operations, while in addition, various new dynamic behaviours have been observed and classified, such as bi-stability, jitter, and smooth running. For mixtures of legacy and autonomous operations, limited capacity gains are observed. Specifically, legacy trains themselves see no gains, in fact they are detrimentally affected when density reaches a certain threshold, where for example, they may become blocked by autonomous trains at converges.
| Date of Award | 13 May 2025 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | R E Wilson (Supervisor) & Nikolai W F Bode (Supervisor) |