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Connected, autonomous and electric vehicles (CAEV) are at the forefront of transport development. They are intended to provide efficient, safe and sustainable transport solutions to solve everyday transport problems including congestion, accidents and pollution. However, despite significant industry and government investment in the technology, little has been done in the way of exploring the implementation of CAEVs in rural scenarios. This thesis investigates the potential for rural road CAEV implementation in the UK. In this work, the rural digital and physical infrastructure requirements for CAEVs were first investigated through physical road-based experimentation of CAEV technologies. Further investigations into the challenges facing the rural implementation of CAEVs were then conducted through qualitative consultations with transport planning professionals. Quantitative and qualitative analysis of these investigations revealed a need for better rural infrastructure, and an overall lack of understanding regarding CAEVs and their rural implementation requirements amongst the transport planning industry. The need for a measurement tool for transport planners was identified, to expose the industry to, and educate them about, CAEVs and their rural potential. As a result, a CAEV Rural Transport Index (CARTI) is proposed as a simple measurement tool to assess the potential for rural CAEV implementation. The CARTI was implemented, and its effectiveness tested, through further consultation with transport planning professionals. The results indicate the potential for the CARTI to be used as a component of decision-making processes at both local authority and national levels. In conclusion, effective rural CAEV implementation relies on transport planners having a strong understanding of rural community transport needs, the solutions CAEV technologies can offer and the supporting infrastructure they require. Further, the CARTI was found to be an effective tool to support the development of this required understanding and recommendations have therefore been made for its future development.

Background: The delivery and effective operation of public transport is fundamental for a for a transition to low-carbon emission transport systems'. However, many cities face budgetary challenges in providing and operating this type of infrastructure. Land value capture (LVC) instruments, aimed at recovering all or part of the land value uplifts triggered by actions other than the landowner, can alleviate some of this pressure. A key element of LVC lies in the increment in land value associated with a particular public action. Urban economic theory supports this idea and considers accessibility to be a core element for determining residential land value. Although the empirical literature assessing the relationship between land value increments and public transport infrastructure is vast, it often assumes homogeneous benefits and, therefore, overlooks relevant elements of accessibility. Advancements in the accessibility concept in the context of Open Science can ease the relaxation of such assumptions. Methods: This thesis draws on the case of Greater Mexico City between 2009 and 2019. It focuses on the effects of the main public transport network (MPTN) which is organised in seven temporal stages according to its expansion phases. The analysis incorporates location based accessibility measures to employment opportunities in order to assess the benefits of public transport infrastructure. It does so by making extensive use of the open-source software OpenTripPlanner for public transport route modelling (≈ 2.1 billion origin-destination routes). Potential capitalizations are assessed according to the hedonic framework. The property value data includes individual administrative mortgage records collected by the Federal Mortgage Society (≈ 800,000). The hedonic function is estimated using a variety of approaches, i.e. linear models, nonlinear models, multilevel models, and spatial multilevel models. These are estimated by the maximum likelihood and Bayesian methods. The study also examines possible spatial aggregation bias using alternative spatial aggregation schemes according to the modifiable areal unit problem (MAUP) literature. Results: The accessibility models across the various temporal stages evidence the spatial heterogeneity shaped by the MPTN in combination with land use and the individual perception of residents. This highlights the need to transition from measures that focus on the characteristics of transport infrastructure to comprehensive accessibility measures which reflect such heterogeneity. The estimated hedonic function suggests a robust, positive, and significant relationship between MPTN accessibility and residential land value in all the modelling frameworks in the presence of a variety of controls. The residential land value increases between 3.6% and 5.7% for one additional standard deviation in MPTN accessibility to employment in the final set of models. The total willingness to pay (TWTP) is considerable, ranging from 0.7 to 1.5 times the equivalent of the capital costs of the bus rapid transit Line-7 of the Metrobús system. A sensitivity analysis shows that the hedonic model estimation is sensitive to the MAUP. In addition, the use of a post code zoning scheme produces the closest results compared to the smallest spatial analytical scheme (0.5 km hexagonal grid). Conclusion: The present thesis advances the discussion on the capitalization of public transport on residential land value by adopting recent contributions from the Open Science framework. Empirically, it fills a knowledge gap given the lack of literature around this topic in this area of study. In terms of policy, the findings support LVC as a mechanism of considerable potential. Regarding fee-based LVC instruments, there are fairness issues in relation to the distribution of charges or exactions to households that could be addressed using location based measures. Furthermore, the approach developed for this analysis serves as valuable guidance for identifying sites with large potential for the implementation of development based instruments, for instance land readjustments or the sale/lease of additional development rights.

Background: The integration of different customised value added services (VAS) in warehouse logistics is an increasing trend. This fact requires logistics managers to identify the most appropriate strategies to meet VAS customer demands and to achieve a superior performance over their competitors. However, as VAS are heterogeneous in nature, the customer demand of these services can vary accordingly. Therefore, distribution centres need to develop those supply attributes, which enable them to respond to each particular VAS customer demand. In this context, the choice between lean and agile supply attributes, which are two contradictory concepts, comes especially into focus. The role of such concepts has been broadly introduced in the context of supply chain performance in the Fisher's framework (1997) and the following models from different researchers. However, the issue of VAS performance in distribution centres under the perspectives of the concepts of lean and agile supply attributes was not considered in the previous warehouse logistics research. Research Aims: This study explored the topic in question from the perspective of distribution centres. The purpose of this study was to gain a greater insight of VAS applications, and particularly, to understand the contributions of lean and agile supply attributes in the performance of VAS in distribution centres. In particular, the study aimed to reframe the meaning of applications of VAS from the perspectives of logistics service providers, to identify the supply attributes necessary to meet VAS customer demands and to understand how managers of distribution centres align their operational strategies with the VAS performance. Methodology: As the research purpose was explorative in their nature, the empirical process in this study was underpinned by the perspectives of the phenomenological interpretivist paradigm. Thus, a qualitative case study of six distribution centres (six companies) in Germany dealing with VAS was carried out. The data was collected by semi-structured interviews, built on the triangulation of sources and use of quota sampling technique. In doing so, the head manager of distribution centre, the operational manager and the customer manager from each distribution centre were included in the research participant circle. The following analysis of the obtained data was based on the principles of Grounded Analysis method that implied an examination of the data by open, axial and selective coding procedures. Results: The main conclusion of this study is that the nature of customer demands of such VAS can vary quite a lot depending on the particular business cases and therefore, the concepts of lean and agile supply attributes have a crucial meaning in the context of VAS performance in distribution centres. This became especially true as, some distribution centres associate VAS performance with the lean concepts of "standards" and "productivity", while the others link performance of VAS with the agile concepts of "flexibility" and "customer responsiveness". In addition, the concept of "quality" has a special significance in the context of VAS performance regardless of the nature of customer demand. However, as shown by the analysis of empirical data, concepts of "quality" as well as "customer responsiveness" are not adequately considered in distribution centres. Furthermore, the research findings indicated that the "material-related" supplementary activities, which logistics service providers physically perform in the operations of their distribution centres in order to gain financial benefits, are the dominant model of VAS in the modern warehouse logistics. Moreover, the applications of such VAS with more agile nature of customer demand can bring higher financial benefit and different non-financial positive effects, but at the same time can lead to higher warehouse complexity. Implications: This study addressed the research gaps by providing a greater understanding of contributions of lean and agile supply attributes in the performance of VAS in distribution centres. In this respect, this study indicated that the perspectives of Fisher's framework (1997) have high relevance in the particular context of VAS performance in modern warehouse logistics. Moreover, the study identified the significance of different concepts related to the VAS warehouse business such as "lack of non-physical VAS concepts", "peculiarity of non TPL DCs", "issue of quality" and "deficit of customer responsiveness measurements", which need to be further explored by future research.

In Britain, 40% of rail travel uses diesel trains, producing emissions contributing to climate change and air pollution. Monetisation of the negative impacts of these emissions is termed external costs. Hydrogen fuel cells offer a possible solution to reduce rail transport-related emissions. Fuel cell rail is advantageous against diesel rail for reducing emissions, and against electric rail for reducing infrastructure requirements. However, fuel cell technology is currently more expensive, and the emissions reduction potential depends upon the source of hydrogen. This thesis aims to explore the internal and external costs of diesel, electric, and fuel cell rail (with seven hydrogen sources), to determine the option which produces the least emissions, and determine the relative financial advantage of fuel cells as an option for the decarbonisation of British rail. The findings of the analysis show that fuel cell rail with hydrogen produced by electrolysis from renewable electricity is the lowest polluting option, with no emissions at point of use or during fuel production. The financial analysis shows that this renewable hydrogen option also has one of the lowest monetary costs, on a lifetime calculation basis. The thesis assesses the adoption of fuel cell rail in Britain, as a financially viable option in the pursuit of rail decarbonisation.

Increased globalization and a growing world population have a significant impact on the sustainability of supply chains, especially within the food industry. In the food industry, transport logistic activities play an essential role in ensuring global food safety. Specifically, transport logistics is likely the most critical step throughout the food journey from farm to fork because of potential stress that affects the product, such as perishable nature and efficiency requirement, cost optimization, environmental impact, and sustainability. Hence, considering the volume of food transported and distributed globally and the range of participants involved in the process, there is a high complexity of risk factors that threaten the smooth flow and the food product's safety with severe consequences for global business. Although the assessment of the risk factors in an end-to-end food supply chain has emerged as a significant concern in research, Previous studies on the subject had only focused on the risk assessment of food supply chains from the production, post-harvest, and processing chain, there are very few studies on a whole food transport logistic (FTL) chain, particularly from quantitative assessment perspective due to imprecise and uncertainty of data information along with the networks, revealing a significant research gap to address. If the risk factors present in the FTL chain are left unaddressed, it will affect the whole supply chain link of the product and creates a considerable loss to the global economy. The study aims to bridge the knowledge gap by developing new uncertainty treatment models that facilitate the assessment and mitigation of risk associated with the safety of the agro-food product during the transportation and logistics network. Methodology: To meet up the requirement of the research objectives, this research conducts empirical studies among the global Agro-food handling companies in Thailand, the Republic of China, and the Republic of Vietnam and follow the four steps of an effective risk management process, namely risk identification and classification, risk assessment, risk cause and effect assessment and risk mitigation strategies. To ensure the analysis is systematic and inclusive, all the various risk factors associated with the agro-food transport and logistic (AFTL) chain were identified through a careful review of the literature following a Delphi technique with industry experts, to verify the reliability of the identified risk factors. The assessment of the risk was conducted with the data collected via a two-stages of questionnaire surveys and evaluated through the Analytic Hierarchy Process (AHP) and fuzzy rule-based(FRB), and Bayesian network (BN). Thereafter, the risk cause and effect and risk mitigation strategies identified via literature review were validated through a set of empirical studies and evaluated through DEMATEL, Evidential reasoning and Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) technique. Findings: The verified forty-six risk factors are classified into two main categories: Internal risk derived from the internal activities of the AFTL firm (i.e Finance, physical, information, organisation, infrastructure /technology, and supply chain risk) and external risk derived from external events or situation that negatively impact AFTL firm that either occurs naturally or caused by human error (i.e Environmental and security risk). The risk and their sub-criteria were identified through empirical studies after the risk assessment with a fuzzy rule base and Bayesian network. The top priority risks are "deterioration in service quality" "leadership in food safety management" "food supplier transparency" and "adaptation to food standard regulation." Furthermore, thirty-five multi-criteria risk causal variables influencing the service quality were identified and classified into four main groups (i.e. Management, operation,resource and relational) such as "flexibility," "completeness of order," "the correctness of order," "the safety of service delivery," the "security of service delivery," "availability of order information," "a consistent procedure in the handling of orders" and "timeliness of shipment, pickup and delivery" among the indicators influencing the service quality of firm in the AFTL chain. The causal variable indicators (CVI's) such as "openness in information exchange" "company ethical image" "social responsiveness" "equipment efficiency" "correctness of order" "the application of IT and electronic data interface" are the net causal variables which would positively influence the other causal risk variables. To mitigate these causal risk variables, the strategies such as "transformation leadership and top management commitment strategy," "service culture, strategy," "information and analysis strategy" and the "continuous improvement and innovation strategy" are identified through empirical studies. After applying the fuzzyTOPSIS technique, assessment results indicate that "service culture strategy" and "information and analysis strategy" are the most important with strong relevance to the service quality performances of the firm in the AFTL chain. Research implication: This study is one of the earliest to recognize the need for a comprehensive risk assessment in the AFTL chain. It contributes to the AFTL risk analysis from different networks of stakeholders and applies an advanced uncertainty modelling technique to evaluate such diversified AFTL risks with high uncertainty in data in the same framework and provide mitigation strategies to manage the AFTL service quality causal risk variable in an uncertain environment Practical implications- The profile of the risk sources, the risk priority weighting, cause and effect interdependency relationship of the causal variables, quantitative assessment of the CVI's and the prioritization of the causal variables mitigation strategies can be beneficial to decision-makers in the food supply networks, transport logistic service provider, food risk assessor, the internal/external auditors in tackling uncertainty and vague information data to support safety-based decision making in the Agro-food transport logistic supply network in Thailand, the Republic of China and the Republic of Vietnam. The research findings can also be applied in other countries.

Real-time monitoring of drivers' functional states will soon become a required safety feature for commercially available vehicles with automated driving capability. Automated driving technology aims to mitigate human error from road transport with the progressive automatisation of specific driving tasks. However, while control of the driving task remains shared between humans and automated systems, the inclusion of this new technology is not exempt from other human factors-related challenges. Drivers' functional states are essentially a combination of psychological, emotional, and cognitive states, and they generate a constant activity footprint available for measurement through neural and peripheral physiology, among other measures. These factors can determine drivers' functional states and, thus, drivers' availability to safely perform control transitions between human and vehicle. This doctoral project aims at investigating the potential of electrocardiogram (ECG), electrodermal activity (EDA) and functional near-infrared spectroscopy (fNIRS) as measures for a multimodal driver state monitoring (DSM) system for highly automated driving (i.e., SAE levels 3 and 4). While current DSM systems relying on gaze behaviour measures have proven valid and effective, several limitations and challenges could only be overcome using eye-tracking in tandem with physiological parameters. This thesis investigates whether ECG, EDA and fNIRS would be good candidates for such a purpose. Two driving simulator studies were performed to measure mental workload, trust in automation, stress and perceived risk, all identified as modulators of drivers' functional states and that could eventually determine drivers' availability to take-over manual control. The main findings demonstrate that DSM systems should adopt multiple physiological measures to capture changes in functional states relevant for driver readiness. Future DSM systems will benefit from the knowledge generated by this research by applying machine learning methods to these measures for determining drivers' availability for optimal take-over performance.

Since the year 2000 the existing business model of many European airlines has come under extreme financial pressure from a weak and volatile economy, the threat of international conflict, terrorism and pandemics, as well as a high dependence on the supply and price of fossil fuels. Oil based transport is becoming a major sustainability issue due to price and supply fluctuations, geopolitics, and burgeoning environmental concerns. This thesis examines the role of business model innovation in supporting a sustainability transition in the European airline sector. To achieve this aim, I develop a novel framework that links the Multi-Level Perspective (MLP), the STOF model and business model innovation to understand the drivers affecting the European airline industry. I adopt a four-stage mixed-method approach consisting of a quantitative study of published European airline results, a grey literature review, forty structured interviews and a final triangulation. The findings pinpoint the failings of the industry and detail the major issues concerning its sustainability transition to a low carbon industry. A fragmentation towards four business model groups is identified. The results of the research indicate that changes to airline business models are reactions to forces arising from the MLP and do not lead industry transition. The study indicates that a long incremental sustainability transition in an internationally regulated industry using evolving complex technologies is difficult to capture by reworking airline business models, but that the adoption of new technology can advance system transition. The separation of these technologies into operating ´parcels´ could offer a valuable understanding of their contribution to the business model construct. A sequenced framework for industry transition is proposed. The thesis contributes to the limited literature on the role of business model innovation in shaping sustainability transitions and highlights critical issues and challenges facing the airline sector in Europe.

In this research, we adopt a systems-of-systems approach by applying complex network analysis to transport networks, taking Englands' railway network as a case study. Further, we adopt the notion that an understanding of a complex system's higher order structures can be derived from an understanding of its components' interactions. Furthermore, exploring the interplay between the structural and dynamical properties of the network is highly intriguing. The general aim in the first study was to show how the interplay between the topological features of this (railway) infrastructure and the human mobility patterns occurring on the basis of these affects the performance of this particular transport network. We first measured the resilience and robustness of the network, drawing inspiration for our methods, in theoretical terms, from those used successfully with ecological networks. We show how topological resilience measures are strongly correlated to the performance of train operators in the network. Furthermore, we conducted two community detection algorithm comparison studies. The general aim was to investigate which algorithms are producing the best community structure for this context. From our results, it emerged that Label Propagation and Leiden with directed modularity perform very well. Another objective of this research was to investigate the effects of incorporating edge weights (the numbers of travellers across an edge), as well as edge direction. We observed that taking weights and direction into account when detecting community structures might not necessarily have a noticeable impact in general. Therefore, we argue that in transport networks or networks that are similar to ours, the mechanism of the algorithm itself is more important in terms of the results being appropriate for the data and for the purposes behind the detection of the communities, and also for the type of communities required. More important, that is, than the pre-processing of the data.

The relevance of customer value in the decision-making process was defined by Holbrook as follows: "[...] customer value is the fundamental basis for all marketing activity" (Holbrook, 1994, p. 22). The buying decision process starts long before the exchange of money for goods takes place. During the course of acquisition, providers use a variety of contemporary and traditional channels to maintain communication. One of the most popular tools for communication and promotion is still the e-mail Newsletter. Particularly in the area of B2B, the Newsletter cannot be replaced by social media or similar channels. Despite this enduring popularity of the e-mail Newsletter, opening and viewing rates are modest. The aim of this thesis is to identify the reasons why some recipients of e-mail Newsletters read them, whereas others do not, and to help practitioners in B2B improve the impact of their e-mail Newsletters. The current research focuses on the recipients' values as a driver for their decision to open and view e-mail content and applies the theoretical framework of the Theory of Consumption Values (Sheth, Newman, & Gross, 1991a). To avoid knowledge dilution, this study narrows the researched group down as follows: a) Leads (potential customers); b) technical and manufacturing managers and CEOs of small and medium-sized enterprises; and c) the German market. The research employs an inductive and qualitative approach. In the course of 32 semi-structured interviews, this study identifies the values impacting the recipients' decision to open and view Newsletters. The uniqueness of this study is the application of the Theory of Consumption Values (Sheth et al., 1991a) in the investigation of a problem in B2B. Since the theory has some limitations, previous academic research has applied it exclusively in the area of B2C. However, when developing their theory, Sheth and his co-authors noted one exception that the current research has taken advantage of. The outcome of the research is two conceptual frameworks based on the Theory of Consumption Values. These are the Conditional-Based and the Time-Based Conceptual Frameworks, which can both be applied by practitioners for a better understanding and subsequent improvement of the Newsletter process. As a result, the current thesis closes an academic gap, contributes to knowledge, and provides managerial implications.

The establishment of automobile-centred mobility paradigm over the past decades resulted in significant degradation of social, economic, and environmental sustainability. Mobility as a Service (MaaS) is a novel concept that aims to create a shift in individuals' travel behaviour away from private car dependence maximising the potential of alternative modes. Recent research suggests, however, that transport users are not yet ready to abandon their cars while those who intend to use MaaS may fully substitute not only personal car trips but also public transport journeys with car-based shared use mobility services. This means that MaaS penetration, contrary to expectations, may result in unsustainable travel practices among its users and further aggravate transport related sustainability issues. Therefore, this thesis makes its purpose to develop an in-depth understanding of potential travel behavioural implications of MaaS identifying opportunities and challenges in its ability to create sustainable travel behavioural change and produce evidence-based recommendations for policy makers and mobility providers to support sustainable travel behaviour with MaaS. This study meets its aim by employing a two-stage consequential mixed-methods approach. The first stage is qualitative and targets 40 transport users residing in three different locations around the UK, namely London, Birmingham, and Huddersfield. The data is collected via semi-structured individual interviews and examined using Thematic Analysis. The second research stage comprises of an online quantitative survey, developed using the findings of the literature review and the Thematic Analysis results. A total of 427 useable responses from UK general public were collected and worked through using a combination of Univariate Analysis and Principal Component Analysis followed by Ordinal Regression Modelling. Thematic Analysis has resulted in identification of five important themes affecting and reflecting user intention to commit to sustainable travel with MaaS - Car Dependence; Trust; Human Element Externalities; Value; and Cost, - each of them with distinctive dimensions, expressed as their sub-themes. Forty attitudinal Likert-Scale statements, pertaining to the Thematic Analysis results, were developed and tested at the surveying stage. After undergoing Principal Component Analysis, the attitudinal statements formed eight MaaS attitudinal factors, with the five core themes developed through Thematic Analysis retained and some of their sub-themes becoming independent. The relationships and dynamics between the eight factors, complemented by socio-demographic and past behaviour items, and MaaS induced travel behavioural intentions were explored using Ordinal Regression. Univariate Analysis confirmed that just about 19% of transport users in the United Kingdom would consider not owning a car when equipped with MaaS, while circa 60% agreed they would replace some of their public transport trips with car-based shared use mobility options. Ordinal Regression Modelling demonstrated the ability of the eight factors, combined with past behaviour and, in some cases, socio-demographic variables, to explain circa 40% of variance in these behavioural intentions, with Value, Human Element Externalities, Trust, Cost, and Car Ownership dimension of Car Dependence being the most commonly appearing significant predictors. These findings are used to produce evidence-based policy recommendations, targeting MaaS related individual attitudes and past travel practices, that will help making MaaS an effective tool for enabling sustainable travel behaviour among transport users.

Land policies of nineteenth century United States significantly impacted the indigenous landscape and ways of life in southern Arizona after its purchase from Mexico in 1853. This research is to understand how the land legislation policies were interpreted and implemented, why the culture of the United States supported these policies and what impact they had on the indigenous cultures of the region. Contemporary accounts, land legislation at both regional and national level, as well as recent commentaries have been consulted to understand the prevailing nineteenth century cultural norms which supported and accepted policies that denuded indigenous people of their traditional lands and ways of life. It will be determined that the land policies were deliberately prejudiced in their formation and were aggressive both in the use of and in their results on the indigenous landscape and people of southern Arizona. Both the Tohono O'odham and the Chiricahua Apache indigenous people have lost significant amounts of cultural heritage and ways of life because of their inability to challenge the laws and prevailing culture in the United States between 1853 and 1912, when Arizona became a state. This analysis of a little-known region in the United States could help to explain why the results of nineteenth century land policies are still resonating in both the indigenous experiences and on the land management of the region to this day.

Road networks are critical to society as they support people's daily mobility, the freight industry, and emergency services. However, a range of predictable and unpredictable events can affect road networks, disrupting traffic flows, connectivity, and, more generally, the functioning of society. With the increased interconnectivity and interdependency of the economic sectors, the need to manage this threat is more critical than ever. To this end, stakeholders need to understand the potential impacts of a multitude of predictable and unpredictable events. The present thesis aims at developing a framework to evaluate and understand the resilience (ability to sustain, resist and recover from perturbations) of road networks under a multitude of potentially unpredictable disruptions, and at assessing the role of different network design (e.g. network topology) and operation (e.g. travel-demand distribution) characteristics in road network resilience. To this end, this thesis adopts a hazard-independent approach that considers all possible scenarios disrupting multiple links (more specifically up to a certain number of links). Novel indicators-including a robustness, unsatisfied-demand and resilience indicator measuring the demand-weighted-average increase in travel time in the disrupted network, the proportion of stranded travellers, and the speed of network-performance recovery, respectively-are developed and tested as part of this thesis. A link-criticality-assessment method based on multiple-link failures is also developed to identify the links that should be given priority for pre-event reinforcement and post-event restoration. To assess the effects of network size, topology, and demand distribution on network resilience, the thesis considers a variety of case studies, including artificial networks generated by a random road network model (developed as part of this thesis) and real network models derived from real-world maps. To assess the influence of demand variations, capacity constraints and congestion on network resilience, this thesis performs a resilience analysis of a network under several demand conditions. Finally, to assess the effects of recovery strategies on network resilience and characterize the optimal recovery strategy, this thesis performs a resilience analysis of a network considering all possible link-repair sequences. This research should ultimately contribute to the incorporation of resilience considerations into transport planning and management standards, which currently give priority to transport efficiency-the efficient movement of vehicles through a transport network under normal conditions-rather than the movement of vehicles under disrupted conditions.

Road freight transportation is considered the backbone of country's socio-economic framework and thus its vital to ensure it is working optimally. The research detailed in this thesis is focused on improving the movement of road freight, especially for hazardous goods vehicles via a road tunnel, with the help of Connected and Autonomous Freight Vehicles (CAV-F). The study analyses real-world Dartford Crossing tunnel data to identify the impact of existing check and allow procedures for Dangerous Goods Vehicles (DGVs) and Abnormal Load Vehicles (ALVs) at a tunnel. A near realistic traffic simulation model is developed as part of analysis and is validated against an independent Highways England's Motorway Incident Detection and Automatic Signalling (MIDAS) data. The effectiveness of CAV-F in improving road traffic conditions is measured using different simulation scenarios involving mixed traffic (i.e. CAV-F and conventional vehicles alongside) and different real-world tunnel closure conditions. Once the effective performance of CAV-F is established, this research develops a novel mathematical model aimed at automating the check and allow procedures for DGVs at the tunnel. The mathematical model calculates the geo-reference locations for the placement of cooperative communications between the vehicles and road infrastructure to generate dynamic vehicular gaps. This will allow desired safety gaps between the platoon of DGVs and its preceding and following vehicles enabling isolated travel via the road tunnel to ensure safe and secure passage. The mathematical model is verified for different road layouts determined based on geo-referenced locations, approaching a road tunnel. Using traffic simulation, the results determine if the modulation of vehicles' speeds at identified geo-referenced locations are suitable for desired gap generation. Finally, to conclude the research questions, the second mathematical model is developed to help uninterrupted traffic merging at the junctions, as was observed after the successful gap generation. This model could also be generalised to optimise the traffic merge sequence at a motorway junction. The approach is inspired by the noise cancellation technique which utilises destructive wave interference patterns, where vehicular flow on two merging roads is considered as traffic waves. By analysing the merge sequence of vehicles at the junction from fixed equidistant positions on separate roads, the dynamic phase shifting is applied by modulating the speeds of the identified vehicles which would otherwise approach at the junction simultaneously, leading to queue formation (or collision). The performance of the approach is then measured using a traffic simulation model and are determined against existing real-world traffic flow on motorways for improvements in travel time, and traffic throughput and reduction in congestion, with increasing traffic density.

The Green Belt is probably England's most popular and longstanding planning policy commanding widespread political support locally and nationally yet it is regularly critiqued as one of the main causes of England's Housing Crisis by academics, think tanks and housebuilders. More broadly, following Hall's seminal work The Containment of Urban England (1973), it is often argued that it contributes to wider inequitable outcomes in the planning system, which is disproportionately skewed in favour of campaigners and homeowners. This thesis critically evaluates the extent to which the Green Belt contributes to the housing crisis and whether the policy needs reforming. The broader question of power in planning is addressed through exploring why and how the public and campaigners support the Green Belt and oppose housebuilding generally. It therefore aims to add nuance to, and move forward the often polarised Green Belt debate by focusing principally on the views of planners, which are often under-represented in the Green Belt debate, despite being the main actor in the planning system. The study focuses on the importance of space through a geographically based case study of the West Midlands. The thesis finds that the housing crisis is a complex, multi-faceted problem consisting of multi-scalar factors although the Green Belt exacerbates the crisis in particular locations, especially on the edge of conurbations. It argues that the policy should not be abolished but modified for the 21st century with a focus on sustainability, especially recreation and environmental improvement, and that there should be national conversation on the policy's overall spatial extent and purpose, perhaps as part of a national plan. The study also finds that people primarily support the policy because of popular planning principles and place attachment rather than house prices. There is a gap between the attempted exercises of power and effective power of campaigners with significant circumscription and modulation of power in the planning system. Finally, many of the issues associated with the Green Belt and community opposition to development generally are related to the lack of strategic planning in the current system so the thesis underlines the need for integrated, strategic planning to protect the environment and meet housing need.

Problem Statement: Significant increase of applying artificial intelligence (AI) will substantially change the way supply chain (SC) entities and their subsystems consisting of human experts and technical agents collaborating in the 2020s and beyond to create sustainable competitive advantages. However, the literature review has revealed that insufficient coverage of appropriate conceptual frameworks (CF) for adequately assessing the performance of future SC scenarios in the context of AI and its contribution to competitive value creation (VC). Research focus has been primarily placed on the ability of isolated AI applications to contribute with natural language processing (NLP), computer vision, machine learning (ML), or rational decision-making to increasing SC effectiveness. However, there is more need to assess the mutual interoperability of these AI abilities and other SC descriptors across the entire SC which will contribute with new knowledge to interdisciplinary academic research and practitioners' strategic, tactical, and operational evolvement. So far, there is the lack of a CF which can be used to build propositions on the impact of AI on inter-organisational SC structures and the ability of AI to support emergence in the SC. Therefore, the overall aim of this thesis is to analyse and evaluate the impact of AI on VC in the SC. Research Methodology: Based on a critical realism ontology, the thesis applies an abductive research approach using mixed methods for data collection and analysis. The research design has the following stages: (1) semi-structured interviews and Delphi Study, (2) cross-impact balance (CIB) analysis, (3) propositions and theory building, (4) verification with cooperative game theory. Main Achievements: (1) Literature review allows to contextualise the application of AI in SC and reveals SC mechanisms as a foundation to develop an appropriate CF. (2) Gaps of the existing research are identified. (3) A CF is developed for proposing a network of relationships between relevant SC descriptors in this research. (4) Relationships between these SC descriptors are evaluated and analysed with the purpose to identify future SC scenarios and their performance. (5) Exploring these SC scenarios allows for establishing propositions on future SC mechanisms and their ability to create value. (6) A theory about the impact of AI on VC in the SC is developed. (7) Verification of the proposed CF and the developed theory. Results: (1) The theory based on the CIB-analysis proclaims that AI creates value for the SC through improving ordinary and dynamic SC capabilities. (2) Sustainable competitive advantages will only be achieved with the combination of widely implemented use of AI in forecasting and fully adopted autonomous planning techniques along the entire SC. This combination of knowledge creation and knowledge distribution is the only feasible future concept to leverage sufficient value through the inevitable data-centric approach across the SC. (3) Isolated application of AI-enabled descriptors of the CF leads to the unavoidable long-term demise of SC. (4) The recommended inter-organisational structure to support controlled self-organisation is built on clusters that connect the inter-organisational subsystems at the interfaces between SC entities. (5) AI applications only indirectly contribute to emergence of new SC structures but create value by strengthening the collective behaviour of human experts to find a new equilibrium. (6) Self-learning AI ability in combination with big data allows for improved SC responsiveness and SC efficiency by turning demand-driven SC into forecast-driven SC. (7) AI creates value through keeping SC resources valuable, imperfectly imitable, and non-substitutable. Contributions: The main contribution of this PhD project from the theoretical perspective is the development of a theory that allows academics to evaluate the impact of AI on VC in the SC in a fact-based manner. The system-theoretical structure of the underlying CF allows academics to explore the aspect of SC learning by extended Resource-based View (RBV) and to explain phenomena of the SC reality by scientifically justified propositions. From practical application perspective, practitioners can apply the CF to derive logical dependencies beyond the proposed descriptors to decide on SC resource mix and to initiate studies and practical projects to synchronise process-orientation, decentral coordination, and decision autonomy to leverage first-mover competitive advantages. Limitations: Due to the resource and time constraints of this PhD project, the findings and results of this thesis are only as good as the knowledge and the experiences of the participating experts and the deductive capabilities of the author of this thesis.

The global concerns for road safety have risen over the past decades based on the number of road fatalities and serious injuries that have occurred. Nonetheless, there have been significant gains in reducing the number road crashes that occur in developed countries. However, road crashes still remain a challenge for developing countries, where road safety is not included as a key aspect of road design. This is as a result of road crashes being viewed as a consequence of road transport and not a preventable occurrence in most developing countries, (WHO, 2005). This research begins with introducing the global problem of mass population movement into urban areas and the impact on road users, public transport facilities and urban road design features. It explains the background for the basis of the research project by looking at the existing situation in Jamaica. And at the same time presents a literature review, which looks at research in the areas of road safety, road safety models and public transport facilities. The methodology that follows, demonstrates the development of a new crash allocation star rating model for pub-lic transport pedestrian users at public transport facilities. This by adopting the model development principles of the iRAP methodology. In addition, a needs-based approach was developed to assess the safety needs of public transport pedestrian users at public transport facilities. This was as a result of public transport pedestrian users and public transport facilities not included as a road user type or road attribute in iRAP. Nonetheless, data collection was done using the iRAP methodology. In particular, the data was collected at different IQL levels according to the iRAP road survey methodology. The data considered road attributes, transport facilities such as bus shelters and transport centres and road crash fatalities. Moreover, road crash fatalities that occurred at public transport facilities in the study area of Kingston and St. Andrew on 9 road sections. The assessment process was carried out first using the iRAP methodology which focused on road attributes within the existing iRAP data base. Where road attributes were not identified, the needs-based assessment process was used. In this way, risk factors identified using the need-based approach were to expand the iRAP methodology, to develop a star rating equation for public transport pedestrian users. By extension, countermeasure interventions were introduced where these were not found to be included in the existing iRAP methodology. Furthermore, an economic analysis was done for two options using the FYRR method. The results demonstrated positive FYRRs', however the rate of return on investment depended on the cost to implement the countermeasure identified.

This thesis consists of three essays in behavioural finance. With one essay in chapters 2, 3, and 4, respectively, this doctoral thesis studies the impact of air travel and cultural distance on financial market participants. Chapter 2 examines whether international tourism affects financial market investments. Using data for more than 40 countries, we demonstrate that recreational travel between countries is associated with higher foreign equity investments. Increased foreign investment leads to a reduction in home bias and improved diversification. The impact of tourism is more potent for countries that are farther apart or when home country residents are more risk-averse. Using predictors of recreational travel as instruments, we show that the relation between foreign travel and foreign equity investments is causal. Collectively, these results suggest that tourism has positive externalities in financial markets. Chapter 3 examines how air travel affects analyst coverage and forecast accuracy. Using air travel to proxy analysts' information environment with firms, I find air travel from analyst location to firm headquarter location stimulates analyst coverage of firms. This effect is identified with the initiation of new air routes, and the terrorist attacks and mass shootings near firm headquarter city. The results also show that air travel stimulates optimism analyst forecast, confirming the hypothesis that air travel induces recognition heuristic rather than information advantage. However, the interaction effect of shared analyst coverage and air travel has positive externalities on stock comovement and return predictability. Chapter 4 examines whether the risk preferences of S&P500 CEOs' spouses have a spillover impact on corporate risk-taking, especially when CEOs and their spouses come from different cultural backgrounds. Our hypothesis is motivated by recent research in the social sciences that individual traits can converge over time within closely formed groups. To empirically test the hypothesis, we hand-collect data on CEOs' and spouses' cultural origins and test whether differences in risk-related cultural norms influence corporate risk-taking. We find that firms managed by CEOs married to spouses from relatively more risk-averse cultures will take on less corporate risk. These results are robust to various econometric specifications, including a model with CEO fixed effects in a sample of CEOs with more than one marriage. Overall, our findings suggest that the cultural composition of a CEO's household affects corporate decisions.

This thesis has been written to deliver a contribution to knowledge in the field of measuring the value of Configuration Management (CM). CM is a management method that ensures that the customer receives the product according to his requirements and can operate it according to the specification and functional as well as physical attributes over the entire life cycle. This study will bridge the knowledge gap which exists within this field of study and will provide both theoretical insights for academics and the general research community, as well as practical knowledge for practitioners working within the Aerospace Industry. The methodology used in the study is the constructivist approach. In order to meet the research aims and objectives an exploratory, qualitative data collection process has been adopted using semi-structured interviews. A sample size of thirteen semi structured interviews were undertaken with the experts in Configuration Management of several Aerospace Companies. The main purpose of the thesis was to explore important issues related to the measuring process and optimisation of Configuration Management. A thematic approach was used to analyse the data. From the results, a theoretical framework has been created to show the possibilities of measuring the value of CM, with a view to convincing senior management of the usefulness and practical implications of this discipline. It has been shown that totally new business models are possible through the appropriate use of CM. The existing measuring systems have more or less elaborate metrics. The central result of the work is that not every company needs the same degree of CM, rather a weighting is necessary in the application of CM, which has to be carried out primarily on the basis of the three themes complexity, individualization and potential. A certain level of input from CM application does not lead to the same level of output for every company. It is ensured that the result is relevant for the development potential of the investigated company and there is concrete economic significance for the future.

The High Reliability Organisations Theory (HROT) identifies five key principles for any high reliability organisation, pointing to air traffic control (ATC) and nuclear power plants as cogent examples. Nevertheless, relatively little is known about the current operational point of HROs. The literature surrounding this subject frequently highlights governance and economic regulation as significant factors in dictating HRO principles. These factors are influenced by both the economic pressures that result from shifts in operating points, as well as the intensely regulated world of global aviation. However, research into this topic has thus far failed to satisfactorily address the extent to which HROs are successful in operating in accordance with HROT principles. This gap in understanding leaves this study to pose the question: "To what extent do highly reliable organisations apply HROT principles in daily organisational practice across the two essential business functions Operations and Operational Support against the influence of external influencing factors, especially regulation?". The present dissertation used three different data collection methods: document searches; online surveys; and expert interviews. The purpose of these methods was threefold. First, to explore the extent to which the case organisation, Deutsche Flugsicherung GmbH (DFS), complies with high reliability principles. Second, to determine the degree of integration of the five dimensions of high reliability in the different business areas of the case company. Third, to explore factors that account for a difference between theory and practice. As a result, it can be stated the organisation complies to a very high degree with the principles of HROT across all business functions. There is a difference between operational and non-operational areas in the perception of the principles as well as in the application and level of integration. Whilst both areas provide deep understanding of HROT and show the dimensions in their day to day work, where the ideas originate from is less known throughout the organization, which leads to the conclusion of it being more in the DNA of the organisation rather than taught knowledge. Whilst the knowledge and integration of the principles is high, many employees state that cost-pressure and regulatory burden apply. The organization acknowledges the influence factors, but also provide evidence that it can`t disrupt an HRO's reliability as its basic principle. The dimensions of HROT form a specific mindset that forms a culture within the organization lived and nurtured by the employees regardless of their area of business. This also results in the biggest difference between theory and practice. Although the awareness of HROT can only be explained and expressed by part of the organisations` members, the organizational practice, reflected also in its culture, shows a living HROT. This demonstrates the potential difference between theory and action, with a theoretical understanding of organizational concept unnecessary from a performance perspective. The present thesis could extend the theory by means of survey and expert interview results, since it could show that HROs such as ATC operates according to HRO principles independent from the area of business. The results from the expert interviews show that, in most peoples' opinions, it is the employees' attitude towards safety and resilience that makes ATC so safe.

Road congestion is a phenomenon that can often be avoided; roads become popular, travel times increase, which could be mitigated with better coordination mechanisms. The choice of route, mode of transport, and departure time all play a crucial part in controlling congestion levels. Technology, such as navigation applications, have the ability to influence these decisions and play an essential role in congestion reduction. To predict vehicles' routing behaviours, we model the system as a game with rational players. Players choose a path between origin and destination nodes in a network. Each player seeks to minimise their own journey time, often leading to inefficient equilibria with poor social welfare. Traffic congestion motivates the results in this thesis. However, the results also hold true for many other applications where congestion occurs, e.g. power grid demand. Coordinating route selection to reduce congestion constitutes a social dilemma for vehicles. In sequential social dilemmas, players' strategies need to balance their vulnerability to exploitation from their opponents and to learn to cooperate to achieve maximal payouts. We address this trade-off between mathematical safety and cooperation of strategies in social dilemmas to motivate our proposed algorithm, a safe method of achieving cooperation in social dilemmas, including route choice games. Many vehicles use navigation applications to help plan their journeys, but these provide only partial information about the routes available to them. We find a class of networks for which route information distribution cannot harm the receiver's expected travel times. Additionally, we consider a game where players always follow the route chosen by an application or where vehicle route selection is controlled by a route planner, such as autonomous vehicles. We show that having multiple route planners controlling vehicle routing leads to inefficient equilibria. We calculate the Price of Anarchy (PoA) for polynomial function travel times and show that multiagent reinforcement learning algorithms suffer from the predicted Price of Anarchy when controlling vehicle routing. Finally, we equip congestion games with waiting times at junctions to model the properties of traffic lights at intersections. Here, we show that Braess' paradox can be avoided by implementing traffic light cycles and establish the PoA for realistic waiting times. By employing intelligent traffic lights that use myopic learning, such as multi-agent reinforcement learning, we prove a natural reward function guarantees convergence to equilibrium. Moreover, we highlight the impact of multi-agent reinforcement learning traffic lights on the fairness of journey times to vehicles.

Shared mobility systems have been recently tested and piloted in many cities across the globe, with great potentials to be deeply woven into the fabric of future urban planning. Those systems, not only represent a more sustainable paradigm that can effectively cut unnecessary emissions, but could also bring significant societal benefits by offering a much more affordable on-demand mobility option to the general public. As a relatively new mobility trend, they expand at impressive speeds, pouring more fleet and infrastructure into the urban spaces than ever before. Coupled with the unpredictability of real-world environments, this brings numerous challenges in the deployment and operation of those systems, impacting their usability and practicality. In this thesis, we aim to better understand the structure, process and interaction of the shared mobility system with such dynamics, from a spatio-temporal learning perspective. Particularly, this thesis addresses the following question: How deep spatio-temporal learning can be tailored to improve the prediction, optimisation and actuation of shared mobility systems in the presence of real-world dynamics? The key insight is that such dynamics, although complex, shouldn't be treated independently in an isolated way, but rather be considered and embraced at full stack, from data modelling to inference and learning across the spatio-temporal domain. Specifically, we tackle this in a number of research threads. Firstly, we propose D3P, a novel data-driven prediction framework, which is able to directly model the spatio-temporal dynamics from the data with time-varying graphs, and uses bespoke dynamic Graph Convolutional Neural Networks (GCNs) to accurately forecast the future demand of the shared mobility systems in both short and long terms. We further proposeMANS, a new optimisation approach for mobility infrastructure deployment based on multi-agent neural search, which can effectively discover the optimal infrastructure deployment strategies for shared mobility systems across space and time, so that the services provided are ubiquitous to the users while sustainable in profitability. Finally, we propose ac-PPO, a novel user-incentive based fleet management approach, which uses a deep reinforcement learning paradigm to guide the rebalancing of fleet with cooperative users, and incorporates the dynamics by a new action cascading technique. All the proposed approaches have been comprehensively evaluated through large-scale experiments with extensive real-world datasets, and the results have shown superior performance compared with the state-of-the-art, demonstrating their potential impact on a broad spectrum of application scenarios.

Achieving the United Nations Sustainable Development Goals and economic recovery following the Covid-19 pandemic will require maritime fleet expansion for increased trade flows and supply chain stability. Sectoral growth is inevitable as global trade upsurges and wide-scale infrastructure developments, such as the People's Republic of China's Belt and Road initiative, expand into developing nations. However, increased fleet capacity is also directly proportional to the impact that shipping has on the marine environment. The maritime sector has not been included in the Kyoto Protocol and the subsequent Paris Agreement based on the persistent notion that the International Maritime Organisation (IMO) can adequately regulate pollution from shipping. Critics of the IMO feel that it is failing at the task of aligning the sector with a sustainable development agenda and climate change targets. In particular, the IMO's decision to not cap carbon emissions, but to rather implement a non-prescriptive, performance-based mechanism, technical standard known as the Energy Efficiency Design Index, is contentious given the urgent call to keep the increase in global average temperature below 2°C above pre-industrial levels. However, this thesis does not engage in discourses about the effectiveness of the IMO's pollution regime for shipping. Rather, it views the task of 'greening' the sector as a collaborative agenda comprising of both top-down standard-setting and bottom-up initiatives adopted by private actors. This thesis asserts that the IMO's role in environmental governance is but one component of improving the overall environmental performance of the maritime sector. Instead, this thesis focuses on the greening of everyday transactions between private actors that support crucial shipping activities, many of which may fall outside of the jurisdiction and enforcement powers of IMO member states. A focus on private actors is justified by an overarching theoretical framework of a contract governance approach to contract theory. Contract governance contemplates a wide range of rule and decision makers, recognising that the public law and private law can contribute to restoring party autonomy in furtherance of a common goal. Where there are weak spots in the regulatory regime, private law mechanisms and devices can assist in obtaining the overall objectives of the state-implemented regime. However, this would call for a modernisation of contract law for greater alignment with external frameworks and normative agendas. This thesis poses one major research question: How contractual mechanisms should be developed to facilitate and increase incentivisation of the green shipping transition? This question is placed in various contexts as categorised by a vessel's lifespan. The research question therefore asks how various commercial transactions can obtain this goal. In the pre-voyage phase, a vessel's construction is predominantly supported by financing and shipbuilding agreements. In its voyage phase, the carriage of goods is supported by a number of contracts, including chartering arrangements. When a vessel reaches its end of life, dismantling and recycling sale agreements dictate how the vessel is to be disposed of. All of these essential transactions have the potential to elevate a set of green principles that will guide how parties exercise their freedom of contract.

Food is an essential part of our global existence, and it is the supply of this [food] that comprisesa complex system of suppliers, spread over vast geographic distances. Coupled to this complexity is a increasing demand for food stuffs, with a purported increase in food stuffs of 50% to 2030. However, there is an increasing amount of food fraud occurring, with a recent study citing a European wide increase of 20% fraud with meat products over the preceeding 20 years. Therefore, it is incumbent on supply chain professionals to mitigate risks of food fraud, and look toward existent research to assist. But, there a paucity of research in the area of supply chain food fraud risk and resilience mitigation, with the majority of research being related to scientific testing processes rather than management theory. Therefore, this research study espouses to address this gap utilising a tri-methodology of systematic literature review, Modified Delphi and semi-structured cross case study analysis. In doing so, it engaged with food supply chain professionals and at latter stages food procurement professionals to gain an appreciated into building internal capabilities against food fraud. Finally, this research compares the outputs of the tri-method approach against existent supply chain resilience literature to build a theoretical framework of capabilities of food supply chain resilience, and areas for further research.

Creating a more resilient railway network is crucial to national socioeconomic performance. GB's railways are considered to be a resilient transport network when compared with other modes of transport. However weather and seasonal events can be attributed as the cause of 12% of train delays, on average. There are already well established areas of research into specific relationships between railway assets and weather hazards which have been directed by industry experts, giving a fragmented overview of the resilience of railway assets to weather. The research presented in this thesis has developed a systematic, methodological approach which challenges the preconceived ideas of the impacts of weather on railway infrastructure. In principle, the analysis integrates historic railway infrastructure fault data with high resolution weather data, accounting for location whilst considering the exposure frequency of assets to different weather types. The methodology developed takes a novel, data-driven approach which highlights challenges for Network Rail regarding data quality. This approach has made few assumptions about the relationships between temperature and precipitation and the significant railway asset categories identified. As a result, this analysis has not only supported existing understanding through further interrogation of fault events but has identified assets which have previously been overlooked but are affected by weather hazards. The approach developed provides a scalable and tractable foundation which can easily be employed by railway infrastructure managers, such as Network Rail, to calculate useful asset fault rate thresholds which can be utilised to inform decision making processes regarding asset maintenance and operations. In addition, the results of the analysis have been used to inform recommendations particularly for Network Rail as the Infrastructure Manager but also for the wider railway industry stakeholders to support progress towards a resilient railway for future mobility. This is of particular importance when viewed within the context of future climate change.

This thesis is a body of work applying data science and mathematical modelling to problems in intelligent transportation systems. Utilising data collected from the M25 London orbital, four problems relevant to both industry and academia are considered. In chapter 4 we develop a novel methodology for anomaly detection on road networks. We determine a data-driven region of typical behaviour in the flow-density plane, tracking fluctuations from this to identify anomalies in real time. We find this offers generally comparable performance to existing methods, but is clearly superior when the distribution of speeds conditioned on time of week is bimodal. In chapter 5, we quantify the prevalence of primary and secondary traffic incidents in our data using a novel self-exciting point process. The selfexcitation component suggests 6-7% of incidents are most likely secondary, occurring temporally and spatially in the wake of other incidents. Our modelling further identifies two spatial hotspots and captures commuting patterns in the UK. We are able to apply out-of-sample validation and show the model is statistically defensible. Chapter 6 explores dynamic prediction of incident durations. We find non-parametric neural network models offer strong performance compared to a range of alternative candidates, achieving errors below current industry targets. By exploring feature importance, we find time series prove informative for predictions on short horizons whereas time of day and location do so at longer horizons. We explore an emergent behaviour path planning model in the context of autonomous vehicles in chapter 7. This was developed in conjunction with engineers from Jaguar Land Rover and incorporates practical constraints realworld vehicles must satisfy. Formulating an optimization problem incorporating comfort, safety and progress, we show dynamically solving this results in emergent complex driving behaviours: vehicle following, passing and overtaking. Safety is based on a distributional prediction of drivers behaviours, with its variance indirectly defining properties of the emergent behaviours. Our findings throughout this work offer models and methodologies that can be used to improve the management and better understand the behaviour of existing transportation infrastructure, as well as the development of future technologies.

The rail industry, despite its iconic value and status as a key economic infrastructure, faces significant challenges today: An ageing, historically underinvested system has had to accommodate an unprecedented increase in demand with limited financial resources at its disposal. Now, following the Covid pandemic the same historically underinvested infrastructure has had to address the reality of an unprecedented reduction. In order to meet these polarised challenges, brought sharply into focus by its imminent restructuring (initiated by the recent White Paper) it needs more than ever, to harness the positive socio-economic impact of its interventions on its key resource: its human potential. In this work the author argues that the value of enhanced skill, knowledge and expertise derived from projects should be recognised and incorporated into more meaningful evaluations of project viability and sustainability. The argument draws on the notions of sustainability, the nature of the asset base and a more comprehensive whole life evaluation to inform and make this case. It has been supported through the use of surveys and interviews, within the context of case studies, in order to represent the nature of the relationship between participation in projects and the enhancement of expertise. The findings describe both a strong relationship (80% agreement and strong agreement) between project participation and expertise enhancement as well as providing indications of some of its key qualities. Adding support to the assertion that there is a necessary and contributory causal relationship between project participation and the enhancement of the expertise of the participants. The Interviews, based on grounded theory, also provide further insights through the identification and distillation of themes which emerge as integral to this process and are consistent with the concepts drawn out from the notion of a sustainable intervention in rail assets. Supported by these findings the author concludes that the value derived from harnessing the potential of its key resource in this way needs greater recognition and to be incorporated into a more comprehensive calibration of project value. A more comprehensive form of whole life evaluation, which made greater accommodation of externalities, while at the same time recognising that rather than being an external attribute of asset value, the enhancement of skill, knowledge and expertise is, in fact, intrinsic to it. A recognition requiring a significant shift of emphasis in the realm of project evaluation and governance in order to ensure its rightful place in the effective incorporation and deployment of asset value.

Green Logistics has attracted increased attention from researchers during the last few years, due to the growing environmental awareness. Road Transport is a major factor in climate change and accounts for a large proportion of the total UK emissions, including Carbon Dioxide (CO₂). With traffic and congestion levels growing, efficient routing combined with greener (more environmentally friendly) vehicles will be of great importance. The purpose of this thesis is two-fold: i) to provide an insight into Green Logistics and ways in which green technologies can be combined within the vehicle routing problem and ii) identifying new variants of the Vehicle Routing Problem (VRP) that can be applied to real-life instances; The Platooning Routing Problem with Changing Split Points, and the proposition of a Hyper-Realistic Electric Vehicle Energy Consumption model that can be applied to the E-VRP. A thorough CO₂ experiment was also conducted on a rolling road, providing useful data that future research can use to further increase the accuracy of routing models. The platooning of vehicles proves to be an important technique that can lead to large decreases in fuel consumption and can be easily implemented in most transport systems; the process requires advanced and accurate computer systems that are only now becoming available to manufacturers. The Platooning model is designed and tested within this thesis and it is hoped to spark further interest in this crucial area of research. Extensions to the Platooning Problem include the addition of heterogeneous fleets and how they change the dynamics of the proposed problems, as well as further work on the placement of the critical splitting point. Allowing the consideration of using limited range Electric Vehicles (EVs) as well as Conventional Vehicles (CVs) and Alternative Fuel powered Vehicles (AFVs) can further increase the emission savings and are becoming progressively popular in today's society. We therefore have carried out extensive research around the area of AFV's including detailed battery specifics for EV's. The objective is to minimise the amount of emissions while satisfying the time window requirements of customers maintaining low overall financial costs. The resulting emissions are largely affected by the electricity fuel mix of the country, we found that the indirect EV emissions for a 30kwh EV can vary by as much as 33% throughout the day and as much as 68% throughout the year with different seasons. Various heuristic and metaheuristic solution techniques as well as several classical heuristics are implemented including the Clarke and Wright Savings heuristic algorithm (CWSA), the Sweep Algorithm and the Variable Neighbourhood Search (VNS) method. These heuristic and metaheuristic models are tested on the Christofides et al. datasets and we achieve solutions that are on average 1.67% and 8.5% deviated from the best-known solution for unrestricted route lengths and restricted max route length problems respectively. Following this a platooning model is generated and tested on various datasets, including a real-life example along the roads of the South East of the UK. Platooning proves to bring benefits to the VRP, with the extensions discussed in this thesis providing increased savings to emissions. On three of the dataset problems of the small and medium size problems a significant fuel saving of more than 1% was achieved. With future research and additional avenues explored Platooning can make a significant reduction to emissions and make an impact on improving air quality. The EV model proposed is designed to trigger further research on ultra-realistic energy models with the aim of being applied to a real-life organisation with various constraints including factors such as battery health, travel speed, vehicle load and transportation distance. This thesis provides useful insights into how important the aspect of environmental route planning is, providing advice on tangible and intangible benefits such as cost savings and a reduction in carbon emissions.

Marine ports are the largest single business complex in the maritime sector impacting the coastal, marine, and atmospheric environment. The environmental effects of port operations mostly originate from the vessel and cargo handling operations, and maintenance. Port operations generate marine pollution in many forms (chemical, biological, solid waste, and sedimentation) and present a challenge to all port operators. Because ports are often located near urban areas, the wider impact of port operations on the environment cannot be ignored as it can potentially affect the economy of these areas as a whole. Air pollution is a significant externality for ports located close to urban areas. Around 4.5% and 6.2% of the total SO2 and NOX respectively, emitted by ships are due to in-port activities such as manoeuvring (approaching harbours) and hoteling (at the dock in port). A vessel consumes around 10% of fuel during slow manoeuvring. Assuming around 4.5% and 6.2% of the total SO2 and NOx emitted by ships are due to in-port activities such as manoeuvring (approaching harbours) and hoteling (at the dock in port), simplifying the traffic model hinders the ability to conduct accurate emission assessment and limits the ability to conduct an environmental assessment as a result of increased port capacity. The research aim is to develop a multi-method simulation model of port systems to simulate port traffic for assessing various port challenges like emission, throughputs, etc. The study will develop a mixed simulation model of port systems comprising of marine traffic and associated processes using the port of Liverpool as a case study. The developed simulation model will be used to estimate emission within the case study port. The study developed a multi-method simulation model representing individual actors and specific processes of the entire port system. The developed simulation method integrates two major modelling approaches: discrete-event simulation and agent-based simulation. Due to the complexity within the port, the study focused on the vessel and cargo handling sector of the port because manoeuvring (approaching harbours) is a significant source of pollution. The developed method adopts an object-oriented approach. Object-oriented modelling is an important aspect of the modelling methodology because it supports the reusability and scalability of the developed model as entities are represented as objects with specific characteristics based on their types. This is significant in representing vessel and cargo terminal types. Each vessel type was encapsulated with internal characteristics e.g. passage plan, speed, etc. A terminal developed to handle bulk cargoes is different from a terminal that handles container cargoes. Therefore, agents were developed to represent various cargo terminal types (such as container terminal, bulk terminal, passenger terminal, etc.), with each terminal type possessing its characteristics specific to itself. The method was applied in the study area. AIS data was collected for the Port of Liverpool over the 12 months of 2016. The data provides information on all marine traffic (fitted with AIS) for the Port of Liverpool outer channel (Liverpool Bay) and the port inbound and outbound lanes along the River Mersey. This data set was used to design and validated the simulation model. A maximum of seven vessels was observed to be transiting through the outer waterway, four at the inner and two in the manoeuvring waterway. Vessel transit times and speed variation are observed to be influenced by the vessel traffic density within each waterway. Vessel waiting and dwell time are seen to be influenced by lock availability and the tidal condition of the port. An increase in tidal duration results in an increase in both waiting and dwell time and vice versa. The validation outcome reveals that the developed model also possesses a relative realistic speed changing behaviour when compared to real-world data. The simulation result also shows a realistic relationship with the travel time distribution from the historical data set. The developed model represents the port as an entire system, however, the study only focussed on the vessel handling process. Previous port modelling has witnessed lots of simplification in vessel traffic models, port process models, and exclusions of external condition models over the years, but the object-oriented programme implemented in this study can help solve these issues. Therefore, the developed methodology would enable better models to be integrated.

In this thesis, Computer Science practices and methods including Software Engineering and Artificial Intelligence techniques are explored to incorporate Human Factors and Psychology knowledge in a structured way into agent-based models to model modal shift in a social system. Observations of peoples' behaviours in social systems regarding choice-making suggest that they tend to have preferences among the available alternatives in many situations. Experts in the domain of Psychology have been interested in the relationships that exist between the psychological processes (factors) and peoples' behaviours. Human Factors' experts are concerned with, among other things, the study of factors and development of tools that improve users' experiences. The findings from the literature suggest that the two groups have been working from the perspective of their domains without much collaboration. Also, no known framework or methodology offers the required collaborative modelling support and techniques to model people's emotion as they traverse the system. The aim of this thesis is, therefore, to provide modelling techniques that better support the use of Human Factors and Psychology knowledge in understanding factors that influence travellers' decision-making in travel mode choice so as to stimulate changes in their behaviours. The support also provides collaboration among relevant stakeholders to work on modal shift project in the transport system. The method adopted in carrying out the research reported in this thesis is informed by the descriptive, developmental, and exploratory nature of the objectives of the research. Our novel methodology which includes a framework is named MOdal SHift (MOSH) methodology. Its development process involves the use of design principles that include encapsulation, data abstraction, inheritance, and polymorphism in defining and integrating the Human Factors and Psychology practices into the methodology. The structures and behaviours of the system components are described and documented using the Unified Modelling Language (UML) as a standard specification language to promote uniform communication among a group of experts. The decision variable decomposition module and techniques for deriving travellers' emotions that correspond to their context involved the use of the Fuzzy sets system. The methodology contains guides that include the process map diagram showing the major stages in the methodology as well as the step-by-step development guidelines. To verify and to validate the methodology, two case studies in the transport domain are selected. The first case study aims at demonstrating the use of the framework included in the methodology for policy formulation. The second case study has the goal of demonstrating the use of the methodology for understanding individuals' abilities to satisfy travel requirements. Data Science methods including both supervised and unsupervised learning algorithms are applied at relevant stages of the case studies. The reflection from the cases investigated with the MOSH methodology reveals its novelty in modelling interdependencies among the transport system's constraints and in modelling travellers' emotional state as they traverse the transport system's environment. In addition, the adoption of the standard specification language in the design of the methodology provides the means for easy communication and transfer of knowledge among stakeholders. The use of Software Engineering tools and methods in conjunction with the agent-based modelling paradigm in the MOSH methodology design and development phases promotes the separation of concerns for the interrelated and non-linear levels of organisation within a sociotechnical system. It also promotes extensibility of various aspect of the methodology as a result of the independence among the components and makes reusability of relevant aspects possible when there are needs to use the same functionality in a new project. The agent-based modelling paradigm provides opportunities for investigating the interactions among the agents and the environment as well as providing insights into the various complex interrelated behaviours.

The purpose of this research is to investigate the impact of heavy goods vehicles that are carrying intermodal shipping containers on the traffic flow. The objective is to estimate the available capacity for HGVs on the road at every hour to accommodate the increasing demand due to the expansion of Liverpool container terminal. The author has developed a passenger car equivalent estimation method and a road traffic capacity estimation methods by considering the deceleration and acceleration performances of vehicles, and the methods consider the speed, reaction time, braking competency level of the driver, and road safety. The author has developed an average traffic speed prediction method to facilitate the rescheduling and planning of the traffic operation. The proposed prediction method provides higher accuracy than all other speed prediction methods and facilitates highly efficient rescheduling and planning operations. The author has proposed four level of service methods that consider the safety, prevention of accidents by available reaction time, stopping distance, and the risk of pedestrians sustaining severe injuries or death, and they unique method because they measure the level of service not from the prospective of the user but from the prospective the traffic management and local authority. The methods target the individual type of vehicle and drivers' behaviour and competency level. The results showed that the required time gap to maintain a safe gap between the following vehicle and the leading vehicle ranges from 2.59 to 3.32s for passenger cars and from 2.94 to 4.89s for heavy goods vehicles. The results also showed that the passenger car equivalent for heavy goods vehicles with braking competency level of 100%-50% is 1.32-2.77 that depends on vehicle parameters. The results also showed that the passenger car equivalent for heavy goods vehicles at 64.4km/h with braking competency level of 100%-50% is 1.32-1.65, and 2.41- 2.77 for rigid heavy goods vehicle, and articulated heavy goods vehicles, respectively. The rescheduling results show how that it is possible to meet both of Mersey ports' targets for Liverpool's container port by either building an extra lane of an heavy goods vehicle access only two-lane road in parallel to the current road. However, to improve the traffic flow operation and safety, the second choice will be better because it will keep the average traffic speed above the optimum speed of the road at all times.

This study represents an investigation into understanding the variation in bus map design and the impact of map design preferences in public transportation information portrayal. In many cities, bus services represent a vital facet of easing traffic congestion and reducing pollution. However, with the entrenched car culture in many countries, including Malaysia, the country targeted here, persuading people to change their mode of transport is a significant challenge. To promote this modal shift, people need to know what services are available and where (and when) they go. Bus service maps provide an invaluable element of providing suitable public transport information, but are often overlooked by transport planners, and are under-researched by cartographers. Although bus services are ubiquitous, much more attention has been devoted to metropolitan rail network maps (e.g. Ovenden 2003 & 2015, Roberts, 2012). The earlier part of this study focuses on identifying the cartographic considerations when designing a bus map in a specific context. There are various approaches to the design of bus maps. It is a challenge for the authorities to understand which designs are preferred by the public and which perform best in conveying the required information. Even a quick look at a small sample of bus maps will show that there can be a significant variation in approach. This can vary from whole-network maps to single routes, and from geographically accurate maps to highly schematised representations. To advance the study and understand the cartographic styles of bus maps, it is necessary to develop a robust classification system that considers the critical factors in their design. The method employed was to examine a wide range of bus map examples from around the world, supplemented by considerations of other map classification systems and personal understanding of available options. The resulting classification is multi-faceted, but there are some apparent top-level differences in approach. One main distinction is between Geographical Approach maps and Schematic maps. While schematic maps are extensively used for metro systems, there are quite different considerations for bus maps and while some level of schematisation may be useful, highly schematic bus maps often present difficulties in everyday use. While the earlier research phases identified the significant options in bus map design, to inform design choices, it is also essential to consider what users prefer and best promotes successful use. To help provide guidance, a map usability study was conducted during which bus users and potential bus users had to solve several journey planning tasks using two different designs of whole-network bus maps. The two different designs are the Geographical Approach bus map design and the Semi-schematised bus map design. The distinguishing aspect of these two bus maps is only in their overall map design approach. Both maps have an identical bus route and bus information detail together with the same cartographic representation in terms of classification, symbolisation and typography. While many of the findings have general relevance to all bus map use situations, the usability testing was carried out in Malaysia to ensure the findings were relevant to promoting bus use in that country. 100 respondents were involved in the map usability test. Cultural differences can be a factor in influencing map use performance and design preferences. The outcomes of the usability study were analysed statistically, starting with descriptive statistical analysis of all possible influencing factors of map preferences (independent variable data) like age, gender, travel habits, driving license availability, among others. These data were then cross-analysed with the findings from the map usability test, ranging from the respondent performance based on the correctness of an answer to their opinions about the maps. The analysis includes the ease of use while using the map, the confidence level in using the map and their map design preference. The results show that the respondents prefer to use the Semi-schematised bus map design rather than the Geographical Approach bus map design. Their map preference's selection is well explained by their performance in the journey-planning tasks and subsequent answers to the questionnaire. The Semi-schematic design had a higher degree of task correctness than when using the Geographical Approach design. This degree of correctness that favours semi-schematic design shows the same pattern across all genders, ages, and travel habits, and is decisively supported by the confidence level ratings and ease of map use ratings. Interestingly, respondents across all age groups say they would be willing to make greater use of public transport if there were more of these kinds of maps available. In conclusion, this research has brought much-needed insight into the public transport information provision of Malaysia's public transport systems. There has been lack of research in this field in Malaysia, and the findings from this study have highlighted the benefit of using the right cartographic technique to enhance public transport information provision and improve the use of the public transport system as a whole. Apart from the continuous improvement in physical aspects of public transport systems, a well-designed public transport map that balances the efficient cartographic technique with local cultural preferences. All these measures certainly can help authorities and public transport providers in encouraging the modal shift from the regular use of private cars to more frequent use of public transport systems.

Knowledge of contexts under which infrastructure affects entrepreneurship is often used as important policy tool to inform and improve infrastructural investments, and to boost entrepreneurial activities in developed and developing world countries (DDWC). Research has shown that scholars do not give infrastructure due importance in entrepreneurship studies. This thesis aims to critically evaluate nature and type of the relationships between infrastructure and new venture creation in DDWC in order to achieve the following objectives: i) Further understanding, and develop extant knowledge about infrastructure; its definition, its classification and its importance. ii) Highlight different infrastructure categories as a possible explanation to regional variations in the levels of entrepreneurial activities across DDWC. iii) Demonstrate how to construct an index of the economic infrastructure category and apply it to rank some countries. iv) Evaluate direct and/or indirect effect(s) of dimensions of the economic infrastructure category (aggregated and disaggregated) on some country-level entrepreneurial activities (e.g. entrepreneurial transition, necessity entrepreneurship etc.) in DDWC. First, a systematic literature review (SLR) was undertaken and its findings used to define, then classify infrastructure into four categories, and nature/type of its reported relationships with new venture creation explored. Effects were largely positive and direct for economic and institutional categories, mixed for the social category, and unknown for the technological category. Next, economic infrastructure (EI) index constructed from several secondary data sources (World Bank, IMF, GEM etc.) was used to rank 112 developing world countries. Subsequently, index effect was evaluated on nascent entrepreneurial transition on sample of 42 sub-countries. Ordinary Least Squares (OLS) and Two Stage Least Squares (2SLS) regression techniques were applied, and index was shown to affect said transition only indirectly, through corruption perceptions. Index effect (disaggregate) was also evaluated on motivation-driven entrepreneurship. OLS and hierarchical multiple regression (HMR) techniques were applied on sample of 31 developed world countries. Findings suggested index attributes that enhanced opportunity-motivated entrepreneurship (OME) rather hurt necessity-motivated entrepreneurship (NME), and vice versa. Furthermore, state fragility was shown to mediate associations between some index attributes and OME, as well as NME. Keywords: infrastructure, new venture creation, developed and developing world, entrepreneurial transition, corruption perception, opportunity-motivated and necessity-motivated entrepreneurship, state fragility.

Based on the combined perspective of the transaction cost theory (TCT), the resource-based view (RBV) and the extended resource-based view (ERBV), this thesis presents an analysis of the influence of identified potential determinants of vertical integration on backward integration with suppliers through mergers and acquisitions (M&A). The objectives of the work are to identify and examine determinants that may have an impact on backward integration through M&A and to create a conceptual model that can be used to explain and examine the determinants of such integration through M&A. Drawing on an extensive literature review, six hypotheses were derived. These test the relationship between the determinants of vertical integration, namely asset specificity, environmental uncertainty, transaction frequency, closeness of competences, the VRIN criteria of the supplier, and the VRIN criteria of the supplier's supplier base, and backward integration through M&A. An empirical, quantitative study was conducted in the form of a self-administered online questionnaire addressing the research aim of the thesis. This was sent to managers and decision-makers, who included procurement managers, supply chain managers, chief procurement officers, and supply chain-related consultants. The survey ultimately reached 445 potential respondents and 122 responses were received. The statistics support the positive and significant impact of asset specificity and closeness between the buyer's competences and those required for a specific activity performed by a supplier. The study contributes to the literature by showing that together ERBV, RVB, and TCT contribute to research on the determinants of vertical integration. The constructs examined, particularly asset specificity and closeness of competence, are ones that predict the suitability of M&A with suppliers within the context of vertical integration. The results are also of relevance for practitioners, helping them to consider and appropriately assess such influencing factors when making decisions about company boundaries.

The global railway infrastructure is carrying ever increasing amounts of railway and freight traffic which in turn is causing accelerated rates of infrastructure deterioration. Given the pressure to increase track utilization, the ageing infrastructure on which much of the railway transport systems are founded, and the constrained budgets under which the infrastructure is managed, appropriate maintenance needs to be predicted, prioritized, planned and carried out efficiently and economically. This doctoral research aims to develop a means of appraising railway track maintenance strategies economically while taking into account the associated risks and uncertainties. To this end, this research proposes, a Whole Life Cycle Cost Analysis (WLCCA) under uncertainty, while considering the direct and indirect costs of track maintenance, and the benefits to train operation, users, safety and the environment. The developed risk-informed approach is demonstrated via case studies on three different route types within the UK mainline railway network.

A shift from car travel to active transport can offer benefits to various problems in urban areas, such as public health, traffic safety and congestion. A major disadvantage of active transport (in particular walking) for most people compared to motorised transport is the limited range, which can be complemented by public transport. Therefore, it is important to understand how walking relates to public transport in urban environments. A frequently adopted guideline in public transport planning around the world is that, based on observations, people would be willing to walk up to 400m for buses and 800m for rail transport. Previous research on walk distances to and from public transport however found large variations and mainly considered how these distances are influenced by the type of public transport, the built environment, journey characteristics and personal characteristics such as gender, age, household size and income. The aim of this research project is to develop the understanding of the role of perceptions, neighbourhood deprivation and public transport provision in the variability of walk distances to and from public transport across urban areas. To do this, the walk distance to and from public transport was conceptualised, based on the literature, as an interaction between the demand for travel using public transport and walking and the provision of public transport services and walk infrastructure in the built environment. Both sides were examined in further detail in this research. With regard to the demand side, the perceived barriers towards public transport and walking were investigated using a postal questionnaire survey in four different areas of Birmingham, mainly distinctive for their level of deprivation and railway access. The perceived barriers were questioned in two different ways: by rating sets of predefined barriers and using an open approach based on free associations. The supply side was studied using a smartphone tracking study that gathered detailed travel data of participants in the two least deprived study areas. Whereas walking was associated with many positive aspects, public transport was perceived much more negatively and associated with various barriers, of which travel time, the unreliability of the service, expense and access issues were the most prevalent ones. The perceived barriers to public transport were found to be significantly influenced by the public transport provision in a neighbourhood. Differences in neighbourhood deprivation tended to affect perceptions as well, although this effect was mostly attributable to differences in socio-demographic factors. It was also found that the barriers to public transport and walking were related to each other and that the experience of one mode can influence how other modes are perceived. Overall, it was concluded that the variability in public transport related walk distances is mainly caused by the provision of public transport services (routes and stops). This both directly affects the distances people need to walk as well as the perceived barriers to using public transport and walking, which shape the demand for walking to or from public transport. The directness of the public transport routes in relation to where people live and want to go is of importance, in which the length of the access and egress walks are related to each other.

Word of mouth (WOM) communication is being magnified and amplified on a vast and unprecedented scale by free, easy to use technologies. Where once thoughts and experiences regarding products and brands were shared orally one-to-one or in small groups, WOM has been transformed by social media into electronic word of mouth communication (eWOM) with hundreds of friends and acquaintances. Consumers can now discuss and share their experiences with brands and products on social networking sites such as Facebook. Using the Elaboration Likelihood Model (ELM), this study identifies factors that affect the influence of eWOM in the Facebook News Feed. Argument strength, source expertise, tie strength and purchase decision involvement are identified as important variables in this study and their effect on attitude towards a product and intention to purchase a product are investigated. The study found that contrary to the expectations of the ELM, Facebook users were not using the strength of the argument contained in the eWOM to make judgements about their intention to purchase a product. Users were instead using the heuristic cue of source expertise to inform their purchase behaviour. Tie strength was also used as a heuristic cue to determine whether an eWOM message was worthy of their attention. This study adds to the literature regarding the influence mechanism of eWOM in Facebook and provides further insight for social media marketers.

This research proposes a response model for a disrupted railway track inspection plan. The proposed model takes the form of an active acceptance risk strategy while having been developed under the disruption risk management framework. The response model entails two components working in a series; an integrated Nonlinear Autoregressive model with eXogenous input Neural Network (iNARXNN), alongside a risk-based value measure for predicting track measurements data and an output valuation. The neural network fuses itself to Bayesian inference, risk aversion and a data-driven modelling approach, as a means of ensuring the utmost standard of prediction ability. Testing on a real dataset indicates that the iNARXNN model provides a mean prediction accuracy rate of 95%, while also successfully preserving data characteristics across both time and frequency domains. This research also proposes a network-based model that highlights the value of accepting iNARXNN's outputs. The value is formulated as the ratio of rescheduling cost to a change in the risk level from a missed opportunity to repair a defective track, i.e., late defect detection. The value model demonstrates how the resilience action is useful for determining a rescheduling strategy that has (negative) value when dealing with a disrupted track inspection plan.

Extreme weather affects not only transport infrastructure, but also travel behaviour. Climate change is causing more frequent and intense severe weather events, and thus is increasing the risks to transport infrastructure, services, and travellers. Travel behaviour trends are also in flux due to shifting working and activity patterns, as space-time flexibility and accessibility choice increases, and standard commuting journeys decline. Information and communication technologies (ICT) are one reason for these changing trends in travel behaviour, and, like climate change, create uncertainty in predicting transport operations and travel choices. However, ICT also has the potential to make mobility and accessibility more sustainable and more responsive to climate change impacts. This thesis sets out to identify the opportunities that improving ICT and increasing space-time flexibility create for commuters and other travellers to maintain accessibility, particularly to work activities, that they may better respond to severe weather, risk, and transport disruption, thereby boosting resilience. The research also concludes that through the integration of travel choices and Internet accessibility and by taking action to address spatial and temporal barriers, policy might better support both resilience and sustainability.

It is essential to understand the effects of climate change in the transportation sector because these consequences can lead to severe problems of accessibility and damage to road infrastructure. The effects of climate change reduce access to the road network and cause the deterioration of transport infrastructure. A road asset management regime should ensure that all locations are accessible and connected to the network. Failure to maintain accessibility across the road network hampers social-economic development and increases poverty. The accessibility problem has been addressed by models which, to date, have not explicitly considered the life cycle of the road infrastructure. Therefore, this research develops such a procedure and examines how climate change and extreme weather conditions affect road accessibility. This research developed an infrastructure-based accessibility model with Indonesia as an example and includes maintenance standards and strategies for managing road assets under extreme weather conditions. The model, which was tested extensively, describes accessibility in terms of availability, quality, and reliability of access. Standards and strategies for Indonesia were evaluated using HDM-4, which is the standard decision support tool for road asset management. It was found that the existing standards are adequate to sustain current levels of accessibility. However, to improve accessibility, the gravel roads must be upgraded into sealed roads, and new roads should also be built. The methodology and conclusions of this research could assist road authorities in managing road infrastructure under budget constraints, and subsequently, support regional development through improved accessibility.

Analysing and learning from spatio-temporal datasets is an important process in many domains, including transportation, healthcare and meteorology. However, in recent years, the volume of data generated for such datasets has increased significantly. This poses several challenges for data scientists, including increased processing overheads and costs. Thus, several methods have been proposed for reducing the volume of data stored and processed to analyse and learn from these datasets. However, existing methods fail to take advantage of the spatial and temporal autocorrelation present in spatio-temporal data, incur unnecessary overheads when retrieving the data, or fail to retain information about all instances and features. This thesis introduces several data reduction methods to address these limitations. First, the kD-STR algorithm is introduced, which hierarchically partitions and models the data, thereby reducing the storage overhead of the dataset. This method minimises the storage used and error incurred. Second, this reduction method is adapted for the context of data linking, and an alternative heuristic proposed that minimises error in the features engineered during linking. Third, adapted algorithms are presented for reducing multiple datasets simultaneously, and reducing large datasets in a distributed manner. Through empirical analysis using real-world datasets, the utility of these algorithms is investigated. The results presented demonstrate the data reduction that can be achieved using these algorithms, as well as the impact of using different spatial referencing systems and modelling techniques. Further analysis is presented that demonstrates the effect of error in location and time, noise and missing data on the data reduction. Combined, the algorithms presented offer an improvement over the state-of-the-art in spatio-temporal data reduction, and the analysis presented demonstrates the results that may be achieved for datasets exhibiting a range of characteristics.

Streets and motorways are the basic blocks in the core of our transportation networks. In recent years, increases in available sensory and computing power have allowed us to start massive gatherings of data related to their use and performance, and to obtain insightful information via data science. This, in turn, has increased our ability to create systems that estimate the state of the transportation networks and provide us with control capabilities over it, giving rise to the concept of Intelligent Transportation Systems. These systems aim to provide deeper levels of observability to our transportation networks so that their capacity can be increased without the need of further heavy investment to develop traffic infrastructure, especially in terms of laying new roads and streets. In this thesis we aim to contribute to this process in both urban and interurban settings. Here we propose two different algorithms to estimate and forecast expected travel time in motorways over the long term, ranging from hours to a week. The first of them is centred around the identification of the different traffic regimes and leveraging their specific characteristics to improve estimation and forecasting. The second of them looks further into the differentiation between recurrent and non recurrent congestion from the point of view of statistical analysis in the frequency space, using the natural frequencies of the traffic system to tell them apart and exert prediction. We also delve into how Intelligent Transportation Systems can affect our cities, looking at how reinforcement learning can create independent agents capable of controlling traffic lights at intersections. We do this by first looking at the most effective agent architectures in different junctions of increasing complexity. Then we dive into the difference in performance for agents in charge of vehicular intersections, provided by an array of reward functions that use different measures obtained from the traffic flow. Finally, we expand these systems to also take pedestrians into account, investigating the rewards that produce the lowest waiting times when serving different modes of transportation with opposing needs.

The Unmanned Aerial Vehicle (UAV) is traditionally understood as a militarised technology utilised in combat and counter-terror scenarios, most notably throughout the Global War on Terror. However, more recently, the UAV has undergone somewhat of a transformation in how its processes and rationalities are conceptualised, being more frequently understood as a technology and technique within humanitarian and assistive contexts. Yet this field, like the technology, is still an emergent one, and understandings of how the ‘humanitarian UAV’ is discursively constituted by a widened range of actors and rationalities is somewhat under- developed. This project seeks to examine and analyse the humanitarian UAV’s wide discursive field, the discursive meaning and logics that constitute the humanitarian UAV, and how the humanitarian UAV, through its attendant rationalities and actors, (re)constitutes and (re)produces distinct understandings of humanitarianism. In focusing on the humanitarian UAV’s wide and variegated discourse, the research draws upon the methodological framework of discourse analysis as pioneered by Ernesto Laclau and Chantal Mouffe, which does not narrowly privilege one form of data but instead de-privileges data as a whole and emphasises the importance in engaging with the broader discourse (here, of the humanitarian UAV) as it is articulated across multiple and dispersed nodal points that convey formations of meaning. This research firstly finds, through an examination of the UAV’s ‘ethical’ discursive constitution, that the UAV is increasingly legitimised as a humanitarian technology by distinct actors and bodies, and, although the meanings that their articulations carry are generally underpinned by different logics, rationalities and motivations, there is an overlap regarding certain neoliberal signifiers. The research also finds, through analysing the widely articulated notion ‘democratisation of technology’, that the humanitarian UAV (and humanitarianism more generally) is increasingly constituted by, and helps to reproduce and naturalise, neoliberal-capitalist rationalities and practices. The research finds that these rationalities also serve to negate certain –‘deepened’– understandings of ‘technological democratisation’ that are antagonistic to the logics of neoliberalism, signalling a partial closure of a number of avenues that seek to renegotiate hegemonic, and other entrenched, humanitarian, formations of power. The research also finds that, through humanitarianism’s discursive association with the biopolitical, the rationalities of neoliberalism are incorporated into a humanitarian-biopolitical nexus, facilitating corporatised forms of humanitarian governance, regulation and control. The ‘humanitarian UAV’ is thus established as that which is increasingly engaged in legitimised –though distinct– forms of ‘humanitarian’ practice via the neutralising and encompassing rationalities of neoliberal-capitalism, which further breaches and affects humanitarian practice and humanitarian populaces through its biopolitical lens. The research highlights that these rationalities, in combination with the application of the humanitarian UAV, co-constitutively sustain and reproduce their own processes and logics, thus incrementally transforming humanitarian space into humanitarian- neoliberal/corporatised-biopolitical (‘corporo-political’) arenas of control and regulation, underpinned by market concerns and the entrenching of dominant power arrangements. Overall, this research highlights how neoliberal and biopolitical rationalities increasingly constitute the humanitarian UAV, humanitarianism and humanitarian space. Furthermore, these rationalities are modified through this technology and its attendant logics and practices by virtue of its extended reach into the spaces constituted by bodies of heightened insecurity, precarity and crisis. It is understood, consequently, that this form of (humanitarian) technologisation highlights the reach, elasticity and imminence of neoliberal and biopolitical rationalities across increasingly diverse environments.

The rail industry in Great Britain aims to provide value for money by improving customer service and journey satisfaction for passengers, particularly given the rising cost of rail travel. There is increased innovation in several areas, including the personalisation of information, automatic delay repayment, the provision of on-board Wi-Fi, and importantly, the successful management of disruption. The enhancement of disruption information is a key area of investment and research within the industry. The aim of this project was to investigate the current landscape of information provision during rail travel with a focus on increasing its appeal to all passengers, principally during disruption. Specifically, the main objective was to understand how academic theories of expertise and passenger behaviour can aid in improving the design of the content and delivery of rail information to passengers. Following the review of existing research around information provision and passenger experience, interviews were conducted with both rail passengers and service providers to discuss the main themes found in the literature. The aim was to gain an understanding of the main challenges surrounding information provision, particularly with regards to adapting to differing levels of customer knowledge. It was noted that there were key differences seen – both from the literature and the interviews – between frequent and infrequent travellers, which could be attributed to being a product of extensive travel experience, or expertise. At this point, the first step taken was to investigate these differences in a large-scale manner, with a specific focus on the way in which knowledge and experience impacted information use. For this purpose, three different types of surveys were designed and administered. The first, an online survey, was used to examine the information preferences of travellers, alongside their assessments of their own level of expertise regarding rail journeys, and their travel frequency. Results showed that expertise and travel frequency seemed to only account for some of the variance in the preferences for different types of rail information. However, rail information was grouped into six distinct categories according to participant ratings, with some types of information being rated significantly higher than others. This was followed by a repertory grid study and an online survey. This study aimed to look at expertise and travel frequency as well, but with an examination of disruption support, as opposed to generalised rail information. Results confirmed the distinct differences between self-rated expertise and ratings of travel frequency – while most people seemed to consider themselves as being fairly knowledgeable regarding rail travel, there were clearer differences seen with regards to the more objective assessment of their travel frequency. Particularly, frequent travellers understand the nuances of disruption information better – identifying both “good” and “bad” features of disruption alerts – as opposed to less/infrequent travellers, who only focused on the uncertainty created by the disruption alerts. Additionally, four important characteristics of disruption information were identified. The final survey – a vignette experiment – aimed to look at expertise and frequency, in addition to other aspects of rail travel. The aim was to identify the impact of other factors that could be contributing to information use and disruption management. Two variables were selected: journey purpose and the level of familiarity that one has with their trip. Hypothetical disruption scenarios were presented to participants, with the variation of these factors. This was followed by the rating of the six main types of rail information gathered from the online survey, and the four key disruption characteristics obtained from the repertory grid survey. These were rated based on their “usefulness” within the hypothetical disruption situation. The findings were surprising. There was limited influence of the variables examined, with distinct preferences for specific types of information seen across all passengers, regardless of the factors presented, or their expertise or travel frequency. When it comes to disruption management, it seems to be that everyone’s preferences for informational support are the same. The main theoretical impact of this thesis was with regards to the study of expertise in itself. Expertise, simply put, is hard to study. Travel frequency, on the other hand, is not. This thesis contributes evidence towards the notion of expertise being a product of experience and practice, making frequency of travel an appropriate proxy for the study of expertise within dynamic travel environments. From an industry perspective, these findings led to the development of an information provision template that outlines the basic areas to be covered when presenting information to rail passengers, within both general travel situations, as well as during the occurrence of a disruptive event. This template can be used as a guidance checklist for the development of all forms of information systems, from mobile applications to on-board information screens to station displays. This thesis concludes with recommendations for the application of these findings, and for future research within this area.

The prospect of being able to shorten the route between Europe and the Far East using the NSR as a permanent shipping lane is attracting increasing interest. This is why the use of the NSR is now a major topic, especially in financial circles, amongst politicians, and shipping operators. Numerous assessments to determine the potential cost advantage of using the NSR as a transit route have been conducted throughout recent years. These are, however conflicting in their conclusions and a final answer to the question is therefore lacking. The primary aim of this research is the application of decision-making tools to analyse the current routes of the NSR. Accordingly, this will lead to the development of decision-making techniques that will formulate a tool for shipping companies to select the most cost-effective route(s) for travelling between the Far East and European regions.

Ports are exposed to various risks, both in their internal operation as well as external business interactions with other maritime logistics companies. While traditional safety and security policies are able to deal with accidents, theft, and hazard-based risks in ports, a new risk assessment is urgently required to tackle those that are caused by threats, such as terrorist attacks. Terrorist attacks have always been classified as disruption risk because they pose a bigger risk of causing enormous damage, unlike that of a natural disaster. In contrast to natural disasters, terrorist attacks do not have a similar pattern among them and usually, terrorists will attack a port at its weakest point or where it can cause the highest impact values. Therefore, it is important for port stakeholders to identify and pinpoint which of the port facilities have the highest impact value as any terrorist attacks. This study starts with the classification types of port facilities and their risk of terrorist attacks based on a comprehensive literature review, and through interviewing academic and industrial port security experts relating to a particular port. The identified risks of port facilities under terrorist attack scenarios are then analysed to consider the impact of the terrorist attacks on port facilities and the resilience of the current port security system. Various approaches have been combined and applied in this process, which offer the chance of the birth of some novel and effective risk modelling techniques and assessment tools, such as, using a combination of ETA and BN to calculate the consequence of the attack, create new security function as a main criteria in ER model and showing full step by step calculation using four criteria in ER. The study is about risk assessment of security countermeasures which is important and beneficial not only to academics but also to seaport stakeholders especially port operators. The work is also able to predict the percentage of damage if the risk occurs and allows the practitioners to make decisions on investing security countermeasures based on complex analysis. Although the risk assessment methods are presented on the basis of specific security countermeasures, it is believed that, with domain-specific knowledge and data, they can also be tailored for a wide range of applications to evaluate the safety of other logistics and transport domains, especially those where a high level of uncertainty is involved.

As efficiency and sustainability become more important, researchers are working on new concepts to improve the way logistics are handled in the current Supply Chain Management (SCM). One concept gaining popularity is the delivery of products in urban areas using bicycles. More companies started using bicycles as an alternative transportation mode and face challenges to efficiently satisfy their customers and employees needs. Large cities with uphill roads require transportation systems to take into account the energy needed by cyclists to move. The load carried on push-bikes has to be kept under a certain threshold for cyclists to be able to pedal on ascending roads. Therefore, cyclists have to choose their route differently to vehicle drivers, hence the need for optimised routing. Another concept gaining popularity is the collaboration in logistics between several companies. Collaboration is thought to be an enabler for a sustainable and efficient SCM. One important improvement of modern logistics is the frequent exchange of containers via multiple cross-docks which requires spatial and time synchronisation between different types of vehicles. Since each logistics network has its specificities and requirements, new solutions such as the Physical Internet (PI) arise with standardised PI-Containers and protocols to face this challenge. By connecting several transportation networks through collaboration, the PI is expected to considerably improve the way logistics are handled in the current SCM. Connecting several distribution networks will produce continually varying network conditions arising from traffic growth. Some regions of the network would suffer from bottlenecks that could lead to an increase in transportation costs. One reason is that traditional routing protocols in logistics do not learn from their previous experiences of network problems such as congestion. Therefore, an intelligent network traffic control method is essential to avoid this problem. As routing problems are at centre stage in transportation sciences, they represent a critical research scope to study in order to improve logistics. As a consequence, in this thesis, three research directions involving routing problems are identified and solved to provide more flexible and extended models to the aforementioned research gaps. First, a new problem is introduced to tackle constraints arising for bicycle deliveries. A novel Mixed-Integer Linear Programming (MILP) model and an Evolutionary Local Search algorithm are developed to efficiently solve the problem. Experimental results show the accuracy and stability of the proposed algorithm compared to the CPLEX solver from IBM on a wide range of generated instances. A real-world scenario is also studied to demonstrate the relevance of this method. Second, this research focuses on the way the VRP can be solved while considering an important number of attributes for real-life applications. A rich vehicle routing problem with pickup and delivery including several attributes for the PI is studied. A mathematical formulation is proposed and implemented in CPLEX to solve the problem. The model is then extended to handle multi-objective, uncertainty and dynamism. Multi-threaded meta-heuristics based on Simulated Annealing and Genetic Algorithm are developed with a set of new operators to handle the problem specificities. Computational results on a generated data-set showed that the proposed meta-heuristics are superior to CPLEX in terms of solvability and computational time. A classical benchmark on pickup and delivery problems was also used to validate the proposed method against state-of-the-art methods. The algorithms are integrated into a framework and used to provide solutions for a local company. Third, the paradigm between the digital and physical internet is explored to propose a new routing approach based on packet routing. While deep learning has been demonstrated to be a promising method for solving numerous optimisation problems efficiently, its application on packet routing is relatively new and rare. This research provides a proof of concept and sheds light on new opportunities to design efficient routing protocols for logistics with deep learning. Simulation results demonstrate that the proposed method is able to not only learn the shortest path for deliveries but also to take into account the truck fulfilment rates to improve global efficiency.

Traffic congestion has become one of the most serious contemporary city issues for urban transportation network as it leads to unnecessary high energy consumption, air pollution and extra travelling time. During this decade, many optimization algorithms have been designed to achieve the optimal usage of existing roadway capacity in cities to leverage the problem. However, it is still a challenging task for the vehicles to interact with the complex city environment in a real time manner. In this thesis, we propose a deep reinforcement learning (DRL) method to build a real-time intelligent vehicle navigation system for sustainable and resilient urban transportation network. We designed two rewards methods travel time based and vehicle emissions impact (VEI) based which aim to reduce the travel time for emergency vehicle (resilience), and reduce vehicle emissions for general vehicle (sustainability). In the experiment, several realistic traffic scenarios are simulated by SUMO to test the proposed navigation method. The experimental results have demonstrated the efficient convergence of the vehicle navigation agents and their effectiveness to make optimal decisions under the volatile traffic conditions. Travel time based reward schema perform better in reducing travel time however VEI based show better result in reducing vehicle emissions. Furthermore, the results also show that the proposed method has huge potential to provide a better navigation solution comparing with the benchmark routing optimisation algorithms.

The thesis combines and contributes literature on healthcare, facility management (FM) com-prising the non-medical processes in hospitals, organisational theory and organisational com-munication in light of FM communication in hospitals. It addresses the research gap of non-existing evidence-based research focusing on FM communication in Swiss hospitals for the benefit of FM executives. This is a gap because well-defined organisational communication is a prerequisite for any organisation to function. Nested within business and management research, a mixed-method case study research de-sign was applied, following a primarily inductive approach led by pragmatism. A series of in-terviews with, and a survey addressed to, FM executives with an analysis of FM meeting minutes established characteristics and challenges of formal and informal communication ac-tivities within the context of hospitals’ FM departments. Results show that traditional channels such as e-mail, phone and scheduled meetings domi-nate. Digitalisation is still in its early stages. Meetings and e-mail communication consume substantial resources while there is a risk that these are not used effectively and efficiently. The majority of hospitals’ FM departments do not have specific and consciously aligned com-munication guidelines available. Meeting minutes revealed that the “FM diversity”, by providing a variety of support processes managed by staff with different professional backgrounds and expert knowledge, is not thoroughly used as an asset to collaboratively achieve tasks together. The research implication is a framework optimising communication procedures in Swiss hos-pitals’ FM departments. The framework supports FM executives to check and if necessary, adjust communication procedures within their respective areas of responsibility. The frame-work consists of 10 key elements supporting effective and efficient FM communication. They are put together in a stand-alone 7-page readily understood document. It includes 27 self-check questions raising FM executives’ awareness regarding these elements as a basis to optimise communication activities in their areas of responsibility. The thesis contributes to op-timising FM communication in Swiss hospitals and to knowledge by taking into account the communication requirements within this particular context. Research limitations include that the implications are subject of the particular context of Swiss hospitals’ FM departments. Further does the framework not provide a specific recipe to ensure effective and efficient FM communication. It raises awareness for FM executives to develop communication procedures tailored to their respective areas of responsibility. Nevertheless, the framework does provide a first step as a basis for future research opportunities considering more detailed the “recipe” aspect desired by FM executives.

This thesis investigates the production and transportation of large-scale paintings on canvas, with a particular focus on Venice in the Renaissance. Since there is no specific literature devoted to the subject, it illustrates the object and the field, using three main research questions. Increasingly, the concept of "mobility" is considered one of the essential factors to a more complete understanding of the historical and physical conditions of artworks, as well as to present them to the public, and preserve them for future generations. Chapter I investigates the role of guilds involved in packing and transportation of goods. It brings together for the first time what we know of the various relevant corporations from published literature and archival sources. Chapter II considers the Importance of transport during art production, by analysing how much aspects of transportation were taken into account while the artist was conceiving a large-scale painting on canvas. I share observations about the choice of best materials for supports, the best painting ground to be used on canvases about to be rolled up and shipped, as well as the construction of large-scale canvases (which are the result of several lengths of fabric sewn together). Other important issues concern the physical space where the painters used to work, or when and where they collaborated with the other artisans involved in the genesis of the artwork. Chapter III examines packing material, transportation procedures and damage in transit as revealed by documents regarding several Renaissance painters involved with the packing and shipping procedures of their products. This includes the search for patterns in packing stuffs, the most common modes of transport, the risks of transportation, and the responsibilities of the many parties involved. Chapter IV analyses a group of Venetian Renaissance paintings from the National Gallery of London to ascertain whether paintings show any evidence of damage caused by transport. I focus in particular on the transit of the paintings prior to their arrival at the Gallery, their handling and transportation history, as well as the damage incurred in transit which, in some cases, is still visible today beneath the surface. The final part of the thesis contains an appendix of conversations with restorers, and a database of the National Gallery paintings that I selected as my case studies.