Challenge F: Even More Trains Even More on Time 1 Overcoming The

Challenge F: Even more trains even more on time Overcoming the Constraints caused by Nodes on the Rail Network. John Preston, John Armstrong and Melody Khadem Sameni. Transportation Research Group, School of Civil Engineering and the Environment. Chris Potts , School of Mathematics. Tolga Bektas and Banafsheh Khosravi, School of Management. University of Southampton, Southampton, UK. Abstract This paper will identify and assess innovative approaches to overcoming nodal capacity constraints on the rail network by examining the scope for technological improvements and operational changes. This will include examination of incremental changes, such as improved design of points, changes in signal spacing and overlaps, but also more radical changes including concepts from other modes (e.g. intelligent speed adaptation) and a relaxation of the Rules of the Route/Plan. A layered approach is adopted by examining nodes of increasing complexity on Great Britain’s national rail network. This paper will focus on work undertaken on the South West Main Line (London Waterloo – Southampton Central) but will also consider the application to more complex nodes on the East Coast Main Line between Huntingdon and Grantham. Our methodology will consist of two main elements. First, we will provide a state of the art review which will examine how nodal capacity problems have been tackled to date in Britain and overseas. We will also briefly examine systematic approaches to innovative problem solving, as proposed by the TRIZ methodology, general systems theory and the theory of constraints. Second, we will develop a generic meso-level model and simulation tool, based on RailSys, which will determine train routeings and schedules, levels of disruption and reactionary delay and measures of capacity utilisation at nodes. We will also outline two alternative approaches. The first set of these focuses on micro-level optimisation by applying production scheduling techniques to rail scheduling, such as shifting bottleneck procedures and local search approaches. The second set of approaches involves integrating simulation and optimisation models by using multi-commodity integer programming formulations. We will present some preliminary findings on the scope for technological solutions (such as enhancements to signalling, switches and crossings) and operational solutions (such as dynamic traffic management) to enhance nodal capacity and overcome bottlenecks. 1. Introduction This paper is based on the OCCASION (Optimising Capacity Constraints: A Simulation Integrated with Optimisation of Nodes) research project, funded by the UK’s Engineering and Physical Sciences Research Council (EPSRC) and Rail Safety and Standards Board (RSSB), that commenced in October 2010 and is due to be completed in September 2012. OCCASION aims to identify innovative approaches to reducing and overcoming nodal (i.e. junction and station) capacity constraints on railway networks, making use of both technological and operational improvements, and to combine simulation and optimisation tools to produce an integrated assessment of possible means of reducing the effects of these constraints. More details of the OCCASION project are provided in Armstrong et al., 2011a. The theory of constraints indicates that the capacity of a system is dictated by the potential capacity of its key constraints or bottlenecks (McMullen, 1998). For the railway industry, the key constraints are imposed by the nodes (junctions and stations) (see, for example, Pachl, 2009, and Hansen and Pachl, 2008). At junctions, capacity is reduced by the traditional design of points (and the time required for them to be set to the correct position) and by rules permitting trains to move (which assume a speed profile that will allow trains to stop before reaching a junction). In turn these rules are determined by factors such as sighting distance, braking distance (including a safety margin) and overlaps. At stations, capacity is lost by the routeings into platforms (essentially an extension of the issues related to junctions), by signal spacings and technology at stations and by dwell times, which in turn are related to passenger embarkation and 1 Challenge F: Even more trains even more on time disembarkation times. This project will examine the extent to which technological changes (e.g. in-cab signalling, closed loop control, improved point design) and operational changes (including changes to the operational planning rules) can limit these capacity losses. The overall objective of the project is thus to identify means of increasing the capacity of nodes (i.e. junctions and stations) on the network without making major investments in new infrastructure (i.e. grade separation, provision of avoiding lines, etc.). For example, work by Cho (2009) has shown that dynamic rescheduling tools can obviate the need for additional infrastructure at key junctions on Britain’s East Coast Main Line (ECML) such as Cambridge Junction, north of Hitchin. OCCASION will examine whether this finding also holds for stations, such as Peterborough (also on the ECML), which may be viewed as complex nodes. This objective can be approached from various directions and by a range of techniques, but the focus of OCCASION is on the use of simulation and optimisation techniques to identify the potential benefits of improved train scheduling and routeing through nodes, and to investigate the potential benefits of technological improvements (such as improved switches and crossings) and relaxations and/or amendments of train planning and timetabling guidelines (the ‘Rules of the Plan’ in the British operating context). Given the above, the structure of the paper follows the broad structure of the OCCASION project (see Figure 1) and is as follows. In section 2, we provide a succinct literature review. In section 3, we provide some preliminary results on the application of simulation models to the nodal capacity issue, with respect to a case study of the South West Main Line and a future case study ofthe ECML. In section 4, we examine some alternative approaches including micro-level optimisation methods and tactical planning models. In section 5, we draw some preliminary conclusions. Capacity enhancement measures Meso-level simulation tool Developing a Tactical Literature conceptual model Case review for analysing planning studies model capacity at nodes Micro-level optimisation tool Figure 1: Conceptual outline of the OCCASION project 2. Literature Review Relevant recent work in the UK includes that undertaken for the 2007 Rail Technical Strategy (and the 2010 update) (TSAG, 2010) and for RSSB (Barter, 2010), along with work we have already undertaken (Khadem-Sameni et al., 2010a). Network Rail’s Route Utilisation Strategies, or RUSs, are also relevant, notably the recently-published draft RUS for consultation for London and South East England, which includes detailed coverage of the South Hampshire and Solent area) (Network Rail, 2010). Our initial review indicates that practice in Britain lags behind that in continental Europe, with a focus on increasing on-train capacity through the use of higher seating or standing densities, longer trains and on the automation of routine tasks, such as the use of Automatic Route Setting (ARS) to assist signallers and controllers. Indeed, it seems that recent and current focus on train punctuality has led to increased journey times through the provision of recovery time and allowances, and thus in increased consumption of line, route and network 2 Challenge F: Even more trains even more on time capacity, with rail users experiencing improved punctuality, but at the expense of increased journey times. We are therefore keen to make use of recent theory and best practice developed outside the UK: for example, the Dutch Triple A program advocates a different approach to operations (in particular by removing conflicting movements), to capacity allocation (by examining marginal claims) and to capacity enhancements (through the use of dynamic traffic management) (Kraaijeveld, 2009a). With respect to dynamic traffic management, we are interested in the use of zones of concentration and compensation in Switzerland (Lüthi, 2008) and the related concept of slot slipping, developed in the scheduling of personal rapid transit systems (Lees Miller, 2011). We are also investigating innovative problem solving techniques to the capacity challenge such as applications of general systems theory (Skytnner, 2001) and use of the 40 principles of TRIZ (Altshuller, 1999). For example, removing conflicting movements might be related to TRIZ principle 2 (taking out), marginal claims may be related to principle 10 (preliminary action) and dynamic traffic management might be related to TRIZ principle 23 (feedback). Following Barber et al. (2007), our initial review of software tools for capacity assessment and management has identified several candidate models. These include the following: (i) RailSys; (ii) OpenTrack; (iii) VISION; (iv)DONS (and its CADANS, STATIONS and SIMONE modules); (v) PETER; (vi) ROMAN; (vii) VIRIATO (and its CAPRES capacity module); (viii) DEMIURGE; (ix) RAILCAP; (x) CMS (Capacity Management System); and (xi)RTC (Rail Traffic Controller). Of these, the first three are microscopic simulation tools which can be used to assess the performance of different infrastructure and timetable combinations, running under planned and perturbed conditions. While they can identify conflicts, and perform some re-routeing under perturbation,

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