Train planning in a fragmented railway: a British perspective

Train Planning (also known as railway scheduling) is an area of substantial importance to the success of any railway. Through train planning, railway managers aim to meet the needs of customers whilst using as low a level of resources (infrastructure, rolling stock and staff) as possible. Efficient and effective train planning is essential to get the best possible performance out of a railway network. The author of this thesis aims, firstly, to analyse the processes which are used to develop train plans and the extent to which they meet the objectives that they might be expected to meet and, secondly, to investigate selected new and innovative software approaches that might make a material difference to the effectiveness and/or efficiency of train planning processes. These aims are delivered using a range of primarily qualitative research methods, including literature reviews, interviews, participant observation and case studies, to understand these processes and software. Conclusions regarding train planning processes include how the complexity of these processes hinders their effectiveness, the negative impact of the privatisation of British Rail on these processes and the conflicting nature of objectives for train planning in the privatised railway. Train planning software is found not to adequately support train planners in meeting the objectives they are set. The potential for timetable generation using heuristics and for timetable performance simulation to improve the effectiveness of train planning are discussed and recommendations made for further research and development to address the limitations of the software currently available

UK rail transport: a review of demand and supply

Over recent years the UK railway industry has seen unprecedented growth in the number of passengers and the amount of freight carried. Expansion in network capacity, however, has not kept pace with this growth. This has led to significant overcrowding and little or no capacity left to run more trains within existing stock or track provision. The UK government however has stated that as part of achieving ‘best value' it wants to further increase rail traffic, and has recently set out a strategy to optimise this. This paper reviews the issues associated with the growth in passengers, the demand placed on the network and the policy developed to accommodate and manage it. It identifies the capacity constraints and options identified for capacity enhancement. The paper concludes that while privatisation has made coherent decision-making difficult there is significant experience to be gained in the development of policy and route utilisation strategies

Train timetable generation using genetic algorithms

The scheduling of railway trains has been a research problem for many years. Many of the choices required are not known a priori and require exploration of the problem to determine them. A modular Genetic system was designedmake the evaluation function and preparation of the timetable tractable. The Genetic system consists of a Genome, split into Chromosomes so the extra choices that become known throughout the evolution can be added to the Chromosomes. A weighted fitness function and a multiobjective non-dominated fitness function were tried, and then partial objective ranking was added. The system has tackled a mixture of problems has produced promising results

Automating rolling stock diagramming and platform allocation

Rolling stock allocation is the process of assigning timetable schedules to physical train units. This is primarily done by connecting together schedules at their terminal locations (known as schedule associations). Platforming allocation is the process of assigning those associations to particular platforms. A simple last-in, first-legal-out algorithm is used for rolling stock allocation that performs comparably to the traditional manual approach but only takes a few seconds as opposed to days or weeks in many manual cases. A simple stochastic hill-climbing approach is used for assigning associations to platforms to provide a conflict-free platform allocation within a few seconds. These two approaches are tested on real train planning problems with excellent results that would allow an expert to rapidly produce optimal or near optimal solutions. The time saving using these approaches can be used by the train planner to try out various options or have greater checking of robustness of the solutions created

Allocating railway platforms using a genetic algorithm

This paper describes an approach to automating railway station platform allocation. The system uses a Genetic Algorithm (GA) to find how a station’s resources should be allocated. Real data is used which needs to be transformed to be suitable for the automated system. Successful or ‘fit’ allocations provide a solution that meets the needs of the station schedule including platform re-occupation and various other constraints. The system associates the train data to derive the station requirements. The Genetic Algorithm is used to derive platform allocations. Finally, the system may be extended to take into account how further parameters that are external to the station have an effect on how an allocation should be applied. The system successfully allocates around 1000 trains to platforms in around 30 seconds requiring a genome of around 1000 genes to achieve this

High occupancy routes and truck lanes

High occupancy routes and truck lane

Reducing railway noise levels: the impact of forthcoming European legislation

Noise from railways has typically been dealt with by using the ‘we were here first’ argument. In the UK that continues to be largely effective, except where major refurbishment is undertaken or new lines are envisaged. Forthcoming EU legislation will require a change of approach. This paper looks at the background and sets out what will now be required: the immediate impact will be an obligation to undertake noise mapping to gain a better understanding of railway noise levels; then action plans will have to be produced to control and mitigate excessive noise (although as yet there is no indication of what will be regarded as ‘excessive’)

'Context, capital and care': Understanding and alleviating pressures to support successful research higher degree completions in regional Australia

This pilot research project was undertaken on behalf the Council of Australian Postgraduate Associations (CAPA) and the Regional Universities Network (RUN) to investigate the experiences of higher degree by research (HDR) candidates at RUN member institutions and to recommend strategies to address HDR non-completions in regional Australian contexts.  Relatively little research has been undertaken on Australian HDR non-completions. Previous studies have focussed on the experiences of Australian postgraduates in general education settings (that is, non-specific to research degrees). Targeted studies about HDR attrition have also been published, but these are for international settings. These projects have revealed many factors influencing completion rates, including student demographics; enrolment type; relocation requirements; financial, family, or health-related barriers; difficulties with supervisor relations or the research environment; as well as academic and project difficulties or disengagement. By contrast, the departures of Australian research candidates from their degrees—either voluntary or involuntary—remains poorly understood. This is a critical issue especially for regionally-based research candidates, given the importance and impact of their research to regional communities and industries, as well as the essential value of having a research workforce with an appreciation of, and willingness to conduct, quality research in regional Australian settings.  The main objective of this project was to identify and investigate the multiple and intersecting factors that influence whether a HDR candidate successfully completes their degree, with a specific focus on understanding the issues and challenges evident in regional Australian settings. The work also sought to make evidence-based recommendations for government, universities, and postgraduate bodies, and inform data collection tools for future research on postgraduate research non-completions.  The title of this report ‘Context, capital, care’ encompasses three important messages that emerged from the data analysis. Firstly, the context of these HDR candidates must be recognised with respect to their regionality, their nature as researchers rather than coursework enrolments, and the complexity of their lives. Secondly, there is a need to provide research candidates with the social, economic, and intellectual capital to thrive within regional university settings. Thirdly, it is important to refrain from viewing these candidates through a lens of ‘enrolment and attrition statistics’ but rather to recognise the complexity of demographic and personal challenges that may impact on their aspirations for achieving a research degree.</p

Normalised viral load analysis of CCHFv RNA by RT-PCR.

<p>A129 mice vaccinated with MVA-1974, MVA-GP or which received sera, CD3⁺ T cells, or both from MVA-GP immunised animals were challenged with CCHFv either 14 days after the booster vaccination or 1 day after transfer of immune mediators. Four days post-challenge, three randomly selected animals from each group were killed humanely and analysed by RT-PCR for levels of CCHFv gene expression (normalised to mouse HPRT gene expression). Each point represents the mean value of triplicate measurements in an individual animal. Lines show mean values and error bars denote standard error.</p

Normalised viral load analysis of CCHFv RNA by RT-PCR.

<p>A129 mice vaccinated with MVA-1974, MVA-GP or which received sera, CD3⁺ T cells, or both from MVA-GP immunised animals were challenged with CCHFv either 14 days after the booster vaccination or 1 day after transfer of immune mediators. Four days post-challenge, three randomly selected animals from each group were killed humanely and analysed by RT-PCR for levels of CCHFv gene expression (normalised to mouse HPRT gene expression). Each point represents the mean value of triplicate measurements in an individual animal. Lines show mean values and error bars denote standard error.</p