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The University of Southampton

Research project: Rail Capacity and Demand

Currently Active: 

Rail demand in the UK has grown strongly since the mid-1990s. This has led to capacity shortages, particularly at nodes on the network such as stations and junctions. Research is being undertaken to assess the extent that simulation and optimisation tools can address this issue, whilst a series of studies have been undertaken to improve rail demand forecasting.

Overcoming Capacity Constraints: A Simulation Integrated with Optimisation at Nodes (OCCASION) was funded by the RSSB, EPSRC and DfT and had objectives to identify and investigate innovative methods of increasing the capacity of nodes (i.e. junctions and stations) on the railway network, without substantial investment in additional infrastructure.

To this end, a state-of-the-art review of recent and on-going work in this area was conducted, and tools were developed to (i) assess existing and predicted levels of capacity utilisation at nodes, thus filling a gap in the current assessment ‘toolbox’, and (ii) investigate improved options for re-routeing and re-scheduling trains, with a view to reducing capacity utilisation levels and making more use of the capacity potentially available, including consideration of the interactions between timetable changes at adjacent nodes. Using a case study of Peterborough and the East Coast Main Line, these tools provided solutions to deliver reduced levels of capacity utilisation by eradicating scheduled waiting time. This could then be used to increase service levels and/or service reliability. Incremental changes to existing railway technologies (e.g. improved points) and operating practice (e.g. relaxations of the Timetable Planning Rules) were investigated, as were concepts from other modes (e.g. road and air transport) and sectors (e.g. production scheduling). The capacity utilisation analysis tools have since been used in collaboration with Arup in the Capacity Charge Recalibration for Network Rail and has been further developed in a Knowledge Transfer Secondment (KTS) on Rail Nodal Modelling and Testing.

A train graph for Peterborough area

Developing Integrated Tools To Optimise Rail Systems (DITTO) will continue the process of developing optimisation formulations, algorithms and processes that make better use of existing capacity without compromising service reliability. It is part of an industry wide initiative called FuTRO (Future Traffic Regulation) and is related to the development of in-cab signalling and the adoption of the European Rail Traffic Management System (ERTMS). It has the following four key components: (i) Development of optimisation tools that maintain safe operating conditions and do not exceed theoretical capacity limits. (ii) Quantification of the trade-offs between the provision of additional train services and the maintenance of service quality so as to develop working timetables that optimise capacity utilisation without compromising service reliability. (iii) Combination of dynamic data on the status of individual trains to produce an optimal system-wide outcome in real time. (iv) Use of Artificial Intelligence to examine tractable solutions to real-time traffic control.

The project is funded by RSSB (Rail Safety and Standards Board) and inolves a consortium of three Universities (Southampton, Swansea and Leeds). There is industrial support from Arup, Siemens Rail Automation and Tracsis.

The work at Southampton will primarily focus on computer modelling. We will develop analytical methods to calculate capacity utilisation indices and relate these to the propagation of delays. This will be used to optimise train timetables using a stochastic version of the job shop scheduling algorithm. A dynamic simulation model, Tracula, developed by the University of Leeds and based on their car following model, Dracula, will be used to adjust train running speeds in real time. This micro-simulation will be linked to a macro-assessment of the network, based on solutions to the Multi-Commodity Network Design Problem.

These tools will be combined in public domain software called OnTrack (see diagram) developed by Swansea University which will also incorporate safety analyses. The results in terms of the dynamic rescheduling of trains will be compared with what train signallers/dispatchers do in real situations. For road traffic, such expert controllers often outperform existing algorithms. In such cases, machine learning tools can be used to produce new algorithms which can outperform human controllers over an extended period. This will be tested in the rail context.

Rail demand modelling work has been undertaken for a number of bodies including the Department for Transport (in conjunction with Arup and Oxera) and the Association of Train Operating Companies (ATOC). Current work for ATOC, undertaken with Mott MacDonald, is examining the impact of external factors (such as geo-demographics, economic performance and competition from other modes) on rail demand. Past work has included determining the impact of improved reliability, improved station facilities and new stations and services on rail demand and on assessing the use of Geographically Weighted Regression.

Related research groups

Transportation Group
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