Overcoming the Constraints caused by Nodes on the Rail Network

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 such as Reading station (on the Great Western Main Line) and its approaches. 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.