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The University of Southampton

Research project: Rail Capacity and Demand

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Rail demand in the UK grew strongly from the mid-1990s to early 2020. This 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. The Covid-19 pandemic has had a drastic impact on rail demand which in turn has implications for future studies of capacity and of rail operations.

Overcoming Capacity Constraints: A Simulation Integrated with Optimisation at Nodes (OCCASION). This project was funded by the RSSB, EPSRC and DfT between 2010 and 2012 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 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 project for Network Rail and has been further developed in a Knowledge Transfer Secondment (KTS) on Rail Nodal Modelling and Testing.

A train graph for the Peterborough area

Figure 1
A train graph for Peterborough area

Developing Integrated Tools To Optimise Rail Systems (DITTO). This project, funded by RSSB between 2014 and 2017, continued the process of developing optimisation formulations, algorithms and processes that make better use of existing capacity without compromising service reliability. It was part of an industry wide initiative called FuTRO (Future Traffic Regulation) and was related to the development of in-cab signalling and the adoption of the European Rail Traffic Management System (ERTMS). It had 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 involved a consortium of three Universities (Southampton, Swansea and Leeds). There was industrial support from Arup, Siemens Rail Automation and Tracsis.

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

These tools were combined in public domain software called OnTrack developed by Swansea University which also incorporated safety analyses.

Research on Rail Operations.

Our work in this area has continued with a series of projects funded by RSSB. This has included the Data-Driven Robust Timetables project, in conjunction with Network Rail, as part of the Data Sandbox Plus programme (2019-2020). This built on an earlier project on Timetable Resilience for Network Rail (2017). It has also included a project on the Analysis and Attribution of Causal Self-Evident Delays, with CACI, and on Very Short-run Timetable Planning (VSTP), with Bellvedi/Tracsis (2020-21).

Related work includes the TOC15 project, Passenger Numbers in Real Time, that was funded by Future Rail (2016-18). Working in conjunction with Govia Thameslink Railway (GTR) and using the Gatwick Express as a case study, tools were developed to provide better real-time information on train loadings to staff and to passengers. This built on earlier work on The Use of Passenger Loading Data to Influence Behaviour and Provide an Improved Experience for Passengers and Operators alike that was funded by the Rail Research UK Association (2015-16).

Rail demand modelling.

Work has been undertaken for a number of bodies including the Department for Transport (in conjunction with Arup and Oxera), Transport for London and the Association of Train Operating Companies (ATOC – now the Rail Delivery Group (RDG)), in conjunction with Accent, Mott MacDonald, Systra and the University of Leeds. This work has included determining the impact of external factors (such as population and income), improved reliability, improved station facilities and new stations and services on rail demand and on assessing the use of Geographically Weighted Regression. Recent work has included reviews of the Crossrail Revenue Model.

Related research groups

Transportation Group
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