Ditto

This project is being undertaken by the Universities of Southampton, Leeds and Swansea and builds upon separate projects undertaken by the three Universities for the RSSB/EPSRC Capacity at Nodes programme that ran from 2010 to 2012. The three projects were Challenging Established Rules for Train Control (Leeds), Overcoming Capacity Constraints: A Simulation Integrated with Optimisation of Nodes (OCCASION – Southampton) and SafeCap (Swansea).

The aim of DITTO is to make a significant contribution to meeting the requirements of the Future Traffic Regulation Optimisation (FuTRO) programme. DITTO contributes to FuTRO by establishing basic principles and proofs of concept and by developing optimisation formulations, algorithms and processes that will help deliver a step change in rail system performance and help to meet future customer needs. This is being done by taking into account developments in human and automatic control on trains and in control centres (particularly related to the European Rail Traffic Management System) and by making better use of data, particularly with respect to time and the position of trains.

DITTO is funded by RSSB (formerly Railway Standards and Safety Board) from 2014 to 2017 as part of project T1071 FuTRO: Increase Fundamental Knowledge for Optimising Traffic Management – see: http://www.rssb.co.uk/pages/research-catalogue/t1071.aspx

DITTO’s industrial partners include Arup, Siemens Rail Automation and Tracsis.

The research covers 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 DITTO 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 DITTO is quantifying 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 micro-level, so that individual train movements can be optimised within overall system requirements.
4. Network integration – optimised timetables will be produced that can be adjusted in real time through dynamic simulation.  The scope for artificial intelligence to combine optimisation and simulation tools to produce tractable solutions to real-time traffic control will be examined.