Ditto

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Developing Integrated Tools to Optimise Railway Systems

Collaborators:
University of Southampton (Prof. John Preston, Prof. Chris Potts, Prof. Tolga Bektas, Dr John Armstrong and Dr Simon Box), 
University of Leeds (Dr Ronghui Liu, Prof Malachy Carey and Dr Tony Whiteing) and 
Swansea University (Prof. Faron Moller and Dr Markus Roggenbach).
 
Industrial Collaborators:
Siemens Rail Automation, Tracsis and Arup. 
 
This project will combine and build upon the work undertaken by three of the project teams in the RSSB/EPSRC Capacity at Nodes programme (Challenging Established Rules for Train Control, OCCASION and SafeCap) with a view to making a significant contribution to meeting the requirements of the Future Traffic Regulation Optimisation (FuTRO) programme and to UK railrelated research more generally. It will contribute to FuTRO by establishing basic principles and proofs of concept and by developing optimisation formulations, algorithms and processes that will deliver a step change in rail system performance and help to meet future customer needs. This will be done by taking into account developments in human and automatic control on trains and in control centres (particularly related to ERTMS) and by making better use of data, particularly with respect to time and position of trains.
 
 
Ditto work plan picture
 
 
The proposal contains four inter-related and complementary technical strands, with specific aims as follows:
  1. Safety – although FuTRO currently resides in the management layer of railway operations, safety is a fundamental and overriding consideration in operations management and control. The safety strand of the proposal underpins the traffic management strands, allowing optimisation activities to proceed in the knowledge that safe operating conditions are being maintained and that theoretical capacity limits are not being exceeded. The tools developed will also have generic applications to traffic regulation.
  2. Reliability – the trade-offs between the provision of additional train services, and the resultant increases in capacity utilisation, and the maintenance of service quality are an area of particular interest within the industry, and this strand of the proposal aims to quantify these trade-offs so as to develop timetables that optimise capacity utilisation without compromising service reliability.
  3. Dynamic simulation – micro-level data on the status of individual trains will be combined to produce an optimal, macro-level outcome, transmitting the system-wide needs back to the microlevel, so that individual train movements can be optimised within overall system requirements.
  4. Network integration – we will produce optimised timetables that can be adjusted in real time through dynamic simulation. We will examine the scope for artificial intelligence to combine our optimisation and simulation tools to produce tractable solutions to real-time traffic control.
 
Deliverables: