The aim of this thesis is to develop an integrated methodology for investigating the potential for new local railway stations within a given area, with particular emphasis on the use of Geographical Information Systems (GIS). Existing methods for assessing the case for constructing new local railway stations have often been found wanting, with the forecasts produced proving to be inaccurate. A review of previous work in this field has been undertaken and methodologies with the potential to enhance local rail demand models have been identified. Trip rate and trip end models have been developed which are capable of forecasting usage at new station sites anywhere in England and Wales. Geographically Weighted Regression (GWR) has been used to enhance the performance of these models and to account for local variations in the effects of explanatory variables on rail demand. Flow level models have been produced for stations in South-East Wales, with a range of model formulations tested. A survey of ultimate passenger trip origins and destinations was carried out in the same area, enabling the accuracy of theoretical station catchment definition methods to be tested. A GIS-based procedure for locating potential sites for new railway stations within a given area has been developed. This was combined with the results from the demand models and estimates of associated costs and benefits to give a synthesised appraisal procedure capable of assessing the case for constructing particular stations. This procedure was applied to 14 sites in South-East Wales and, along with trip end forecasts for 421 sites across the country, this indicated that there is almost certainly a positive case for constructing a significant number of new railway stations in the UK.

Pandrol Rail Fastenings Limited are a designer and manufacturer of railway rail-fastening systems. As an organisation they have the capability to reduce the noise impact of bridges using resilient track components. They also have a commercial interest in providing such technology. Knowledge of the processes behind bridge noise is important to Pandrol in two ways; to aid the engineers within the organisation in the design of fastening systems and to demonstrate a state-of-the-art understanding of the problem of railway bridge noise to customers, as this will aid in the sale of Pandrol products. The fitting of new rail components to an existing track form, or failure to meet noise regulations with a new track form, can be costly. It is important to be able to predict accurately the effectiveness of noise reduction techniques. Currently, Pandrol’s knowledge of the problem consists almost entirely of experience gained and data gathered while working on existing bridge projects. To expand their knowledge base, Pandrol perform noise and vibration measurements on railway bridges and viaducts and then use the measured data to predict the performance of their systems on other bridges. This completely empirical approach to predicting bridge noise is both costly and situation specific results cannot be provided before the installation of the fastening system. ii Another approach to predicting bridge noise is through the application of analytical models. Limited analytical modelling in the context of bridge noise is currently conducted within the organisation. For these reasons, Pandrol are sponsoring research into bridge noise in the form of this EngD project. Here an existing rapid calculation approach is identified that relies less on the exact geometry of the bridge and more on its general characteristics. In this approach an analytical model of the track is coupled to a statistical energy analysis (SEA) model of the bridge. This approach forms a suitable basis from which to develop a better model here by concentrating on its weaknesses. A mid-frequency calculation for the power input to the bridge via a resilient track system has been developed by modelling the track-bridge system as two finite Timoshenko beams continuously connected by a resilient layer. This has resulted in a power input calculation which includes the important effects of coupling between the rail and bridge and the resonance effects of the finite length of a bridge. In addition, a detailed study of the frequency characteristics of deep I-section beams has been performed using Finite Element, Boundary Element and Dynamic stiffness models. It is shown that, at high frequencies, the behaviour of the beam is characterised by in-plane motion of the beam web and bending motion in the flange. This knowledge has resulted in an improved calculation for the mobility of a bridge at high frequencies. The above improvements are included in an improved model for use by Pandrol in their general activities. Data from real bridges is compared to predictions from the improved model in order to validate different aspects of the model. The model is then used to study the effect on noise of varying many bridge design parameters. It is shown that the parameter that has most influence on the noise performance of a bridge is the dynamic stiffness of the resilient rail fastening system. Additionally it is demonstrated that for a given bridge and noise receiver location, an optimum fastener stiffness exists where the noise radiated by the bridge and track is at a minimum.

The noise level associated with a train travelling on a bridge is normally greater than that for a train travelling on plain track. It is sometimes the bridge noise that causes the highest levels of disturbance to people in the vicinity or triggers action under regulations such as the Environmental Noise Directive. Consequently, there is a need to study means of predicting noise levels from proposed bridges, noise control measures for existing structures and principles of low-noise bridge design. This thesis describes a programme of work in which an existing calculation model for bridge noise and vibration has been tested and alternative calculation methods have been developed where required. The existing model is based on analytical models for wheel-rail interaction and the calculation of the power input to the bridge. The response of the various component parts of the bridge for this power input is found using a simplified SEA scheme. In this work, the existing model has been tested against measurements made on railway bridges and the results of an advanced method of structural analysis, the Waveguide Finite Element (WFE) method. This method is well-suited to modelling some important types of railway bridge. Specifically, it allows a numerical modelling approach to be used up to higher frequency than conventional Finite Element methods. It has been found to offer some significant advantages over the existing bridge noise model, particularly for concrete-steel composite bridges and concrete box-section viaducts. The track support structure has an important influence on bridge noise and vibration, through its role in the transmission of vibration from the rail to the bridge. Laboratory measurements have been made in this work to characterise the vibration transmission properties of two important types of track support structure on bridges; ballasted track and two-stage resilient baseplate track. Improved methods of modelling the dynamic behaviour of these track forms have been developed from the measurements, which can be used in calculation models for both bridge noise and also for rolling noise.

The prediction of wheel wear is a significant issue in railway vehicles. It is correlated with safety against derailment, economy, ride comfort, and planning of maintenance interventions, and it can result in delay, and costs if it is not predicted and controlled in an effective way. However, the prediction of wheel and rail wear is still a great challenge for railway systems. Therefore, the main aim of this thesis is to develop a method for predicting wheel wear using artificial neural networks. Initial tests were carried out using a pin-on-disc machine and this data was used to establish how wear can be measured using an Alicona profilometer. A new method has been developed for detailed wheel wear and rail wear measurements using ‘Replica’ material which was applied to the wheel and rail surfaces of the test rig to make a copy of both surfaces. The replica samples were scanned using an optical profilometer and the results were processed to establish wheel wear and rail wear. The effect of load, and yaw angle on wheel wear and rail wear were examined. The effect of dry, wet, lubricated, and sanded conditions on wheel wear and rail wear were also investigated. A Nonlinear Autoregressive model with eXogenous input neural network (NARXNN) was developed to predict the wheel and rail wear for the twin disc rig experiments. The NARXNN was used to predict wheel wear and rail wear under deferent surface conditions such as dry, wet, lubricated, and sanded conditions. The neural network model was developed to predict wheel wear in case of changing parameters such as speed and suspension parameters. VAMPIRE vehicle dynamic software was used to produce the vehicle performance data to train, validate, and test the neural network. Three types of neural network were developed to predict the wheel wear: NARXNN, backpropagation neural network (BPNN), and radial basis function neural network (RBFNN). The wheel wear was calculated using an energy dissipation approach and contact position on straight track. The work is focused on wheel wear and the neural network prediction of rail wear was only carried out in connection with the twin disk wear tests. This thesis examines the effect of neural network parameters such as spread, goal, maximum number of neurons, and number of neurons to add between displays on wheel wear prediction. The neural network simulation results were implemented using the Matlab program. The percentage error for wheel and rail wear prediction was calculated. Also, the accuracy of wheel and rail wear prediction using the neural network was investigated and assessed in terms of mean absolute percentage error (MAPE). The results reveal that the neural network can be used efficiently to predict wheel and rail wear. Further work could include rail wear and prediction on a curved track.

Sustainability is a broad concept which embodies social, economic and environmental concerns, including the possible consequences of greenhouse gas (GHG) emissions and climate change, and related means of mitigation and adaptation. The reduction of energy consumption and emissions are key objectives which need to be achieved if some of these concerns are to be addressed. As well as being an important component of sustainability in other sectors, a good transport system needs to be sustainable in its own right. Energy consumption and GHG emissions are important issues within the transport sector; in the European Union (EU), for example, transport is directly responsible for between 25 and 30 percent of all carbon dioxide (CO2) emissions, and the inclusion of indirect (Scope 2 and Scope 3) GHG emissions may increase this proportion further. If reduction targets are to be met, it may be necessary to encourage behavioural change, including modal shift from those modes of transport which are comparatively highly polluting, towards those modes which pollute less. Rail is potentially a suitable target for such modal shift from road transport (notably the private car for passenger travel) and, in some case, from short-haul and domestic aviation. However, modal comparisons are often based on average data, and are reliant on a number of assumptions. There are likely to be some circumstances where modal shift towards rail makes more sense than others, but the use of average data does not enable policy makers to be discerning. It should also be noted that many modal comparisons are also based purely on operational energy consumption and emissions, and neglect to take the whole life-cycle in to account. Embedded energy and emissions from the construction of vehicles and infrastructure can be quite significant, as can the energy consumption and emissions from vehicle idling in the case of public transport modes. After considering the concept of environmental sustainability, this research begins by reviewing existing energy consumption and emissions data for vehicle operation, where it is noted that data for cars in Europe are quite comprehensive. Manufacturers are obliged to publish fuel consumption and emissions data for each model of car they sell, although the type approval tests do not reflect real-world performance. Studies are reviewed which suggest that the gap between the tests and the real-world has been widening in recent years. The gap appears to be independent of the size of vehicle, but is larger for hybrid vehicles than it is for those powered solely by a petrol or diesel internal combustion engine. Data for trains are less comprehensive, and that data which are available are often based on a limited empirical sample, or simulated data for which a number of assumptions have been made. Sometimes, the details of the measurements taken or simulation parameters used are unclear. As a result, published data for a particular type of train in the literature are sometimes found to vary significantly. In order to make more informed comparisons between rail and other modes, two large empirical datasets have been analysed. Two UK Train Operating Companies (TOCs) have also made data from energy metering systems on-board their electric trains available, which have been used to analyse the actual energy consumption of different trains over a number of different routes. The sample size is far larger than that found in literature to date, and it has been possible to consider variation between routes and service types. The v basic principles of simulating the energy consumption (and related emissions) of a train have also been illustrated, and a software tool has been developed for Arup so that it can now make some estimate of operational energy consumption and emissions for a given train over a given route. The aforementioned empirical data have also been used to validate the tool and suggest some appropriate simulation parameters. A review of existing literature concerning whole life-cycle analysis has been undertaken. It is clear that life-cycle costs vary significantly but in general, the overall life-cycle costs of rail appear to be higher than those for any other mode. The biggest additional factors appear to be the embedded carbon and energy in the infrastructure, particularly for a system comprising a lot of bridges, tunnels and large underground stations. For the vehicles themselves, trains typically have a longer lifespan than cars, which reduces the embedded carbon and energy as functions of time. When comparisons are made between modes, passenger-km is a metric which is often chosen, because it helps account for some of the fundamental di�erences between modes, including the fact that public transport modes usually use vehicles which are much bigger than the private car. In order to make comparisons on this basis, however, something about the load factor must be known. The sensitivity to load factor is demonstrated, and the earlier empirical data analysis is used to illustrate the benefits of longer trains. A discussion then follows about the potential pitfalls of making comparisons purely on a per passenger-km basis. This thesis ends by summarising some of the �ndings. Some consideration is given towards the future and the fact that technological developments are being made in Sustainability is a broad concept which embodies social, economic and environmental concerns, including the possible consequences of greenhouse gas (GHG) emissions and climate change, and related means of mitigation and adaptation. The reduction of energy consumption and emissions are key objectives which need to be achieved if some of these concerns are to be addressed. As well as being an important component of sustainability in other sectors, a good transport system needs to be sustainable in its own right. Energy consumption and GHG emissions are important issues within the transport sector; in the European Union (EU), for example, transport is directly responsible for between 25 and 30 percent of all carbon dioxide (CO2) emissions, and the inclusion of indirect (Scope 2 and Scope 3) GHG emissions may increase this proportion further. If reduction targets are to be met, it may be necessary to encourage behavioural change, including modal shift from those modes of transport which are comparatively highly polluting, towards those modes which pollute less. Rail is potentially a suitable target for such modal shift from road transport (notably the private car for passenger travel) and, in some case, from short-haul and domestic aviation. However, modal comparisons are often based on average data, and are reliant on a number of assumptions. There are likely to be some circumstances where modal shift towards rail makes more sense than others, but the use of average data does not enable policy makers to be discerning. It should also be noted that many modal comparisons are also based purely on operational energy consumption and emissions, and neglect to take the whole life-cycle in to account. Embedded energy and emissions from the construction of vehicles and infrastructure can be quite significant, as can the energy consumption and emissions from vehicle idling in the case of public transport modes. After considering the concept of environmental sustainability, this research begins by reviewing existing energy consumption and emissions data for vehicle operation, where it is noted that data for cars in Europe are quite comprehensive. Manufacturers are obliged to publish fuel consumption and emissions data for each model of car they sell, although the type approval tests do not re ect real-world performance. Studies are reviewed which suggest that the gap between the tests and the real-world has been widening in recent years. / The gap appears to be independent of the size of vehicle, but is larger for hybrid vehicles than it is for those powered solely by a petrol or diesel internal combustion engine. Data for trains are less comprehensive, and that data which are available are often based on a limited empirical sample, or simulated data for which a number of assumptions have been made. Sometimes, the details of the measurements taken or simulation parameters used are unclear. As a result, published data for a particular type of train in the literature are sometimes found to vary significantly. In order to make more informed comparisons between rail and other modes, two large empirical datasets have been analysed. Two UK Train Operating Companies (TOCs) have also made data from energy metering systems on-board their electric trains available, which have been used to analyse the actual energy consumption of different trains over a number of different routes. This thesis ends by summarising some of the findings. Some consideration is given towards the future and the fact that technological developments are being made in both the motor and the rail industries.

A new failure criterion for discriminating good and bad (overheated) railroad car wheels is proposed. This criterion can replace the conventional “four inch" discoloration rule. The procedure for the new discrimination criterion is based on the fluctuations of the azimuthal residual stress in the tread of the wheel. This criterion is based on a maximum likelihood statistical analysis of data obtained from six different wheels as deterrmined by x-ray diffraction. Of these locations, the analysis showed the tread, and perhaps a critical point on the top of the flange, to be the most sensitive to residual stress. The variance analysis showed that fluctuations in stress at the most sensitive location in the tread appeared to be related to the service history. The residual stresses showed an oscillatory pattern in the hoop direction around the wheels. Extension of the measurement technology to the use of magnetoelastic stress measurement is proposed. To evaluate the inaccuracy in stress data possible from a large sample with curved surface, corrections for a deliberate tilt of the plane of the x-ray diffractometer from the normal to the sample surface have been developed. Analysis of different misalignments are discussed. To validate our x-ray residual stress data, residual stresses were also measured by hole drilling. Excellent agreement between two techniques was found. Finally, stress variation with depth below surface was determined by the hole drilling technique. / Ph. D.

This thesis presents work carried out over a number of years within the field of railway vehicle instrumentation. The railway industry is currently moving to be more heavily “data driven”. This means that railway organisations are putting policies into place whereby decisions have to be justified based on recorded and citable data. To achieve this, the railway industry is increasingly turning to greater and greater levels of instrumentation to deliver the data on which to base these decisions. This thesis considers not only this increased requirement for data, but the frameworks and systems that must be put into place in order first to obtain it, and then to extract useful information from it. In particular the author considers the issue of contextualisation of data, where multiple datastreams may be used to provide context for, or allow more accurate and beneficial interpretation of each other in order to support better decision making. In order to obtain this data, the thesis explores, through a series of case studies, a number of options for different instrumentation system architectures. This culminates in the development of a distributed system of embedded processors arranged in an extensible modular framework to provide a rich, coherent and integrated dataset which can then be processed contextually to yield a better understanding of the railway system.

The railway assets studied in this project, are those widely distributed pieces of equipment that are critical to the dependable operation of the railway system. A failed asset is likely to cause significant delay to rail services, and may even place the system into an unsafe state. A generic fault detection and diagnosis (FDD) solution for a number of railway assets of different types is therefore desired. In this thesis, five assets, namely the pneumatic train door, point machine and train-stop, the electric point machine and the electro-hydraulic level crossing barrier, are considered as case studies. Based on their common dynamic characteristics, these assets are also known as Single Throw Mechanical Equipments (STMEs). A generic FDD method is proposed for these STMEs, which consists of sensor inputs and pre-processing, fault detection processes and fault diagnosis processes. A generic model, composed of a series of sub-models, is constructed to describe the behaviour of each asset. The results of fault detection approaches indicate that the proposed method has good performance and is generically applicable to the five assets. Two fault diagnosis methods using fault model and residual analysis are proposed and the fault model based fault diagnosis is preliminarily approached. Finally, a new three level architecture for railway condition monitoring is discussed for practical applications.

Railways are large, complex systems that comprise many heterogeneous subsystems and parts. As the railway industry continues to enjoy increasing passenger and freight custom, ways of deriving greater value from the knowledge within these subsystems are increasingly sought. Interfaces to and between systems are rare, making data sharing and analysis difficult. Semantic data modelling provides a method of integrating data from disparate sources by encoding knowledge about a problem domain or world into machine-interpretable logic and using this knowledge to encode and infer data context and meaning. The uptake of this technique in the Semantic Web and Linked Data movements in recent years has provided a mature set of techniques and toolsets for designing and implementing ontologies and linked data applications. This thesis demonstrates ways in which semantic data models and OWL ontologies can be used to foster data exchange across the railway industry. It sets out a novel methodology for the creation of industrial semantic models, and presents a new set of railway domain ontologies to facilitate integration of infrastructure-centric railway data. Finally, the design and implementation of two prototype systems is described, each of which use the techniques and ontologies in solving a known problem.

In recent years there has been an increase in railway usage which led to reduced time for rail maintenance. Railway switches and crossings (S&C) are an important asset and they typically account for 30% of the total budget spent on maintenance. The first part of this work researches the feasibility of automatically inspecting S&Cs in accordance with Network Rail inspection requirements and the likely necessary advancements. Current S&C inspection requirements, as well as current and developing inspection solutions, were analysed and categorised. This revealed the required technological advances and likely changes that the railway will have to adopt. The second part of the work researches the weakness of conventional S&C profile inspection practices used in industry. The work identified the main reasons that can lead to poor traditional inspection of the S&C profile, developed a novel, automatic method to carry out the profile measurements which eliminated human error and identified possible improvements in the area of S&C profile inspection. During this research, an inspection trolley was prototyped, field trials were carried out, and good results were obtained.