PhD studentship ‘Intelligent Virtual Network’ EPSRC iCASE

Network Rail, with University of Sheffield Department of Mechanical Engineering, and Department of Automatic Control & Systems Engineering.

University of Sheffield: Dr David Fletcher, Prof. Robert F Harrison
Network Rail: Dr Meena Dasigi

Applications are invited for a fully-funded PhD studentship (3.5 years) to work on a ‘virtual transport network’ for trialling ways to improve railway transport through better understanding of network connectivity, reliability and capacity.

Candidates will need a 1st class or high 2:1 honours degree in a subject relevant to the research field, which includes engineering, maths, computing, or physical sciences. The rail industry has decided to use a PhD research route for this work to bring in skills and technologies that have not traditionally been applied in railway systems. An interest in transport is desirable and good communication skills are essential. A strong mathematical background, and an ability and interest in programming (e.g. Matlab or C) will is desirable.

The work will take place within a team of researchers developing this new and rapidly developing area of transport engineering. Improving rail network performance requires answers to many questions, such as:

• Defining the risks which affect network performance
• Modelling how these risks combine to determine system performance
• Identifying the technical and commercial constraints on performance
• Developing ways the vulnerabilities and risks to the network can be reduced

Trialling potential changes to the rail network on the live system could cause huge disruption. In this project research will be conducted to develop a standard, validated, virtual rail network as a simulation tool - a framework within which traffic management strategies and equipment (infrastructure, signalling, vehicles) of differing characteristics and reliability can be tested and compared.

Technical challenges will include mathematically representing operation of key items in the transport network (track, signalling, vehicles, staff) in good, degraded or failed condition. Probabilistic quantification of equipment failure rates, and understanding the implication of these failures for the overall network will include considering re-routing of services, and the methods to minimise the number of passengers affected. Methods may include autonomous agent modelling, graphical modelling and game theory, and will build on existing research regarding network optimisation for time keeping and energy use reduction.