It is important to study accidents and their underlying causes, in order to generate recommendations for improving system safety. A range of methods have been developed in various industries, to understand how accidents have occurred, as well as identify potential human errors in systems. Theories of accident causation, and the development of safety models and methods have evolved over the last few decades. However, the majority of accident analysis methods fail to account for the increasing complexity of socio-technical systems (Hollnagel, 2004 and Lindberg et al. 2010). Much of the previous research has taken a safety I perspective, which considers successful performance as reducing the number of adverse outcomes to as low as possible (Hollnagel, 2014). According to Hollnagel (2014) however, it is important to understand how operators actually carry out work (‘work-as-done’), rather than as it should be carried out (‘work-as-imagined’), to understand how normal variabilities and flexibilities in performance contribute towards both successful and unsuccessful performance. Understanding how work is normally carried out is essential for understanding how it can go wrong. This includes understanding how success is obtained, for example how people adjust their performance in the face of changing conditions and demands, and limited resources (such as time and information). Although variability and flexibility in performance are prerequisites for success and productivity, these can also explain why things can go wrong (Hollnagel, 2014). Understanding normal work (or ‘work-as-done’) is the basis of the safety II perspective, which views safety as increasing the number of things that go right. So far however, there seems to be little application of this safety II perspective in the rail industry. Research in this thesis addresses this gap, by examining whether understanding normal performance in rail engineering contexts contributes towards identifying how incidents occur, and measures for improving safety, compared to the use of existing methods. A range of different methods were used to address the aims of this thesis. Rail incident reports were analysed to understand sources of human errors in rail contexts. Observations were also conducted of operators carrying out work, to understand the opportunities for human errors associated with rail engineering processes. To understand cognitive demands and strategies associated with normal work, a cognitive task analysis was carried out. FRAM (Functional Resonance Analysis Method) (Hollnagel, 2012) wasalso used to determine how incidents may develop, and whether everyday performance can contribute towards successful and unsuccessful performance. Participants in semi-structured interviews and workshops were asked to identify strengths and limitations of various human reliability assessment methods, and offer opinions on their practical applicability. Benefits of understanding normal work included a greater understanding of how human errors can occur (by identifying cognitive demands that contribute towards the occurrence of different error types), and how cognitive strategies can reduce human errors and contribute towards acceptable performance. It was demonstrated how variabilities and flexibilities in performance can contribute towards successful and productive performance, as well as explain why things can go wrong (supporting Hollnagel, 2014). This is especially important to consider, since human errors were not easily identified from rail incident reports and observations of operators carrying out work. System safety can therefore be improved by increasing things that can go right, rather than just decreasing the things that can go wrong (Hollnagel, 2014). Participants in a workshop, however, identified that FRAM may be time consuming to apply, especially for more complex systems. Further research is recommended for the development of a toolkit, from which both practitioners and researchers can choose from a range of different methods. To further understand factors affecting acceptable performance, it is recommended that further data are collected to determine whether varying levels of cognitive demands affect performance, and whether these influence the implementation of cognitive strategies.

The issue of managing a large and complex railway system with continuous traffic flows and mixed train services in a safe and punctual manner is very important, especially after disruptive events. In the first part of this thesis an analysis method is introduced which allows the visualisation and measurement of the propagation of delays in the railway network. The BRaVE simulator and the University of Birmingham Single Train Simulator (STS) are also introduced and a train running estimation using STS is described. A practical single junction rescheduling problem is then defined and it investigates how different levels of delays and numbers of constraints may affect the performance of algorithms for network-wide rescheduling in terms of quality of solution and computation time. In order to deal with operational dynamics, a methodology using performance-based supervisory control is proposed to provide rescheduling decisions over a wider area through the application of different rescheduling strategies in appropriate sequences. Finally, an architecture for a real-time train rescheduling framework, based on the distributed artificial intelligence system, is designed in order to handle railway traffic in a large-scale network intelligently. A case study based on part of the East Coast Main Line is followed up to demonstrate the effectiveness of adopting supervisory control to provide the rescheduling options in the dynamic situation.

The rail industry has focused on the improvement of maintenance through the effective use of online condition monitoring of rolling stock and rail infrastructure in order to reduce the occurrence of unexpected catastrophic failures and disruption that arises from them to an absolute minimum. The basic components comprising a railway wheelset are the wheels, axle and axle bearings. Detection of wheelset faults in a timely manner increases efficiency as it helps minimise maintenance costs and increase availability. The main aim of this project has been the development of a novel integrated online acoustic emission (AE) and vibration testing technique for the detection of wheel and axle bearing defects as early as possible and well before they result in catastrophic failure and subsequently derailment. The approach employed within this research study has been based on the combined use of accelerometers and high-frequency acoustic emission sensors mounted on the rail or axle box using magnetic hold-downs. Within the framework of this project several experiments have been carried out under laboratory conditions, as well as in the field at the Long Marston Test Track and in Cropredy on the Chiltern Railway line to London.

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.

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.

In the last twenty years the availability of vast amounts of data has enabled industries to gain insight into numerous aspects of their operation whose trends were previously unknown. The result is an unprecedented ability to predict operational needs, to evaluate performance of individuals or assets and prepare such industries for uncertainties. The rail industry currently produces large amounts of data that are, in many cases, not used to their full potential. The first case study demonstrates a novel method to identify and cluster distinct driver styles in use on a DC rail network. Using the optimal driver styles identified, improved ‘driver cultures’ were designed that are shown to provide up to 10% energy savings without the need for expensive in cab driver advisory systems. The second case study details data taken from a full fleet that were used to develop a statistical method to identify the minimum amount of vehicles that required energy metering whilst still providing an accurate mean energy consumption estimate. The identification of this minimum amount was then used to validate the fleet size intended for partial fleet metering options for UK rail networks.

I address the problem of the high capital cost of high-speed railways and the need to reduce their energy use through the design of robust operations at the planning stage. Given the cost structure and benefits of different solutions, reducing the size of termini and maintaining robust operations in and near the termini is identified as a promising option for cost reduction. Two methodologies from manufacturing industry, namely, the Lean principle for cost reduction and the Taguchi method for robust design, are confirmed as suitable tools to realise the objective of improving the design of high-speed railways. I developed a novel approach that combines Lean and Taguchi techniques to deal with characteristic features of high-speed railways, such as the severe requirement for robust operations. Finally, the worth of the combined approach has been demonstrated by means of case studies of current British conventional railway practice, current Japanese high-speed railway operations and the planned High Speed Two (HS2) line. The latter work has shown the possibility of a reduction in the proposed number of platforms at Euston Station, the main terminus of HS2 in London, as well as energy saving for traction.

The advent of modern railway signalling and train control technology allows the implementation of advanced real-time railway management. A number of researchers throughout the world have previously considered the problem of minimising the costs of train delays and have used various optimisation algorithms for differing scenarios. However, little work has been carried out to evaluate and compare the different approaches. Firstly, this thesis compares and contrasts a number of optimisation approaches that have been previously used and applies them to a series of common scenarios. It is found that simple disturbances (i.e. one train delayed) can be managed efficiently using straightforward approaches, such as first-come-first-served. For more complex scenarios, advanced methods are found to be more appropriate. For the scenarios considered in this comparison, ant colony optimisation performed well. Secondly, in order to improve the currently available algorithm so that it can more reliably find optimal or close to optimal results within a practical computation time, a new hybrid algorithm, based on ant colony optimisation, has been developed. In order to evaluate the new approach 100 randomly generated delay scenarios are tested, and a comparison is made between the results of the new algorithm and first-come-first-served, brute force and standard ant colony optimisation. It is shown that the hybrid algorithm has improved performance in terms of optimality and computation speed. Finally, a new multi-stage rescheduling approach for finding an optimal solution over multiple junctions is proposed. A case study is considered, and it is shown that the proposed approach performs well.

Effective maintenance of railway track is critical for the safe operation of any railway network. Efficient maintenance may also result in economic benefits for rail operators. The work in this thesis looks into how an inexpensive measurement system could be fitted to in-service railway vehicles such as commuter trains, to provide a relatively high frequency of measurement on their routes of operation, when compared to dedicated measurement vehicles. This thesis describes how a prototype inertial measurement system was designed and built, and fitted to a commuter train operating in the region south of London, UK. Inertial data is processed to provide a vertical profile of the track. A novel use of a modified Bryson-Frazier filter is used to produce vertical profile datasets which are repeatable to within 0.2 mm. Profiles calculated from multiple passes of the same areas of track are compared to show track degradation. Methods of estimating track stiffness are developed using vertical geometry data from repeated passes of the same track sections at differing speeds. Some correlation to stiffness is shown through the results, but exact measurements were not possible. Finally, two case studies are presented which show findings at a bridge approach, and through two level crossings.