Application of soft systems methodology to frame the challenges of integrating autonomous trains within a legacy rail operating environment

Abstract

Abstract Increased demand on rail, due to climate initiatives and passenger numbers, places significant pressure on existing railway operations; specifically on capacity, operational flexibility, and network robustness. These pressures are exacerbated by constraints, which prevent the construction of new track and infrastructure. This results in the need to use existing infrastructure and operating processes. One proposed solution is digitalization, which results in autonomous rail, where automated and connected intelligent transport systems facilitate smart traffic management. However, this generates the challenge of integrating autonomous trains and their associated technologies with existing infrastructure and operations. To understand this issue from an enterprise level, this paper has applied Brian Wilson's Soft Systems Methodology (a variation of Checkland's methodology) to the problem situation. This methodology explores and investigates the existing rail system in Great Britain (UK less Northern Ireland) and its stakeholders. The paper aims to propose a solution into how to transform the legacy system into one which incorporates autonomous operations and ultimately becomes fully autonomous. It culminates in a series of models that are relevant to those with a legitimate interest in the system. The models identify the activities required to analyze whether autonomy is worthwhile and if it is, how to successfully integrate it with legacy operations. Additionally, the models provide the basis for which a formal stakeholder analysis can take place.