Development of semantic data models to support data interoperability in the rail industry

Railways are large, complex systems that comprise many heterogeneous subsystems and parts. As the railway industry continues to enjoy increasing passenger and freight custom, ways of deriving greater value from the knowledge within these subsystems are increasingly sought. Interfaces to and between systems are rare, making data sharing and analysis difficult. Semantic data modelling provides a method of integrating data from disparate sources by encoding knowledge about a problem domain or world into machine-interpretable logic and using this knowledge to encode and infer data context and meaning. The uptake of this technique in the Semantic Web and Linked Data movements in recent years has provided a mature set of techniques and toolsets for designing and implementing ontologies and linked data applications. This thesis demonstrates ways in which semantic data models and OWL ontologies can be used to foster data exchange across the railway industry. It sets out a novel methodology for the creation of industrial semantic models, and presents a new set of railway domain ontologies to facilitate integration of infrastructure-centric railway data. Finally, the design and implementation of two prototype systems is described, each of which use the techniques and ontologies in solving a known problem

How can we know a self-driving car is safe?

Self-driving cars promise solutions to some of the hazards of human driving but there are important questions about the safety of these new technologies. This paper takes a qualitative social science approach to the question ‘how safe is safe enough?’ Drawing on 50 interviews with people developing and researching self-driving cars, I describe two dominant narratives of safety. The first, safety-in-numbers, sees safety as a self-evident property of the technology and offers metrics in an attempt to reassure the public. The second approach, safety-by-design, starts with the challenge of safety assurance and sees the technology as intrinsically problematic. The first approach is concerned only with performance—what a self-driving system does. The second is also concerned with why systems do what they do and how they should be tested. Using insights from workshops with members of the public, I introduce a further concern that will define trustworthy self-driving cars: the intended and perceived purposes of a system. Engineers’ safety assurances will have their credibility tested in public. ‘How safe is safe enough?’ prompts further questions: ‘safe enough for what?’ and ‘safe enough for whom?

Urban Mobility Transitions: Governing through Experimentation in Bristol and New York City

Transitions away from car-dominance is one of the key debates in urban research, policy and practice today. Car-free public space, cycling and convenient public transport services are widely seen as desirable, yet the reconfiguration of our streets and transport networks has been incremental. This doctoral research examines how mobility in cities is governed through experiments, commonly understood as pilot projects, and whether experiments hold potential for transformative change in urban mobility systems, including transitions away from automobility. The research draws on a synthesis of sustainability transitions, transport studies and urban studies literature, and traces the outcomes of 108 experiments undertaken over two decades in two cities: Bristol (UK) and New York City (USA) between 1996/7 and 2016. The findings demonstrate that experiments can contribute to transforming the physical shape of urban mobility systems and the institutions involved in governing them, and can even contribute to transitions, if assessed as change in commuting patterns away from car use. The research compares the capacity of respective municipal governments, Bristol City Council and NYC city government for ‘transformative experimentation’, and presents an institutionalist analysis of why the transformation of Bristol’s mobility system was more limited than NYC’s. To unpack the problematisation of piecemeal, ‘project-based’ experimentation driven by competitive funding landscapes, the research compares Bristol City Council and NYC city government as two municipalities with a different degree of reliance on external funding. The stronger capacity of NYC city government can be explained by its higher degree of fiscal autonomy and mobility policy discretion, whereas Bristol City Council’s capacity was limited by the centralisation of the UK state. Yet the thesis also shows that both municipalities pursued successful endogenous strategies in response to multi-scalar structure, and points to organisational and governance practices that can create ‘political space’ for urban actors to further transitions