Public transport is crucial to economic activity, functioning cities and access to work, but presents many pinch-points (doors, confined areas of queuing, ticket gates) where social distancing is easily compromised. These points determine people flow rates, creating conflicting priorities in enabling functioning transport while maintaining social distancing safety. The proposed research will build on previous agent-based modelling of passengers at the railway platform-train interface conducted using massively parallel Graphics Processing Unit (GPU) simulations for parameter exploration and sensitivity analysis. Our current RateSetter model has informed rail sector policy and stakeholders through collaboration with Railway Safety and Standards Board (RSSB). Additional factors to be explored include: (i) Incentives such as imminent train departure to compromise social distancing. (ii) Limitations on personal situational awareness in complex confined space pedestrian flows. (iii) Differing personal assertiveness and its impact on confined space flow dynamics. Modelling will focus on optimisation of passenger flow to avoid incentivising compromised social distancing, providing guidelines on effective timetabling and COVID safe station operation. This is expected to be very important in a semi-lockdown situation as large numbers of rail passengers are likely to be in the later cohorts to receive any vaccination yet will want to begin travelling again. To convert the findings to actionable insights for policy and practice validated predictions of passenger flow times for train boarding and alighting under a range of conditions will be transferred to RSSB for input to network level rail system modelling. This will reveal the network wide implications of behavioural change and management of passenger flow at individual stations. RSSB will facilitate data access, knowledge exchange and dissemination within the rail industry. The work will increase confidence in rail use and enable higher passenger volumes with lower risk of compromised social distancing through: (i) Algorithms representing human movement in confined spaces subject to incentives to compromise social distancing. (ii) A validated model to rapidly test and optimise new ways of operating transport to aid national recovery. (iii) Guidelines on quantification of intervention effectiveness in limiting proximity and cumulative proximity (potential viral load) for passengers and staff. (iv) Input of validated passenger flow time predictions to rail industry network wide modelling to reveal impacts of station management policies.

Safety performance of the UK railways has improved significantly in recent decades, though over the same period costs have also increased, resulting in an efficiency gap between current industry costs and comparable railways elsewhere in Europe (McNulty 2011). This gap can be partly attributed to the use of overly conservative safety standards, with the potential to be replaced by calculated risk controls accompanied by appropriate risk modelling and assessment. By identifying low risk locations or routes and areas where the risk controls deliver little or no additional safety benefit, the industry could achieve significant cost and efficiency savings through the removal of unnecessary/over-prescriptive control measures (Griffin & Holloway 2012). The proposed research will deliver a model and toolkit to improve prediction of risk to rail infrastructure using environmental information, including weather conditions in real time or, historically, according to user specified scenarios. This adds several new dimensions to RSSB's Safety Risk Model (SRM) and thus provides significant potential to improve risk calculations and controls and provide cost efficiencies for managing the safety of the UK rail infrastructure. The proposed research identifies clear potential advantages to the RSSB stakeholder, who stand to benefit from a much improved Safety Risk Model, taking environmental factors into account. Such benefits would be shared with other rail industry stakeholders, including Network Rail, Department of Transport and other researchers, all informing government rail safety policy, investment and spending decisions. If successful, the developed tool has the potential to be "disruptive", changing "business as usual" practices at the RSSB stakeholder and related rail infrastructure organisations. The tool can provide the introduction of real-time environmental safety risk modelling which has not previously been available in the UK, or reported to be in place elsewhere.

The research on optimisation problems in network environments has a long history but it generally fails to capture real-world scenarios as it usually assumes that both the network environments (such as network topologies, node processing capabilities, interference, etc) and the optimisation problems (such as the user requirements) are known in advance and remain unchanged in the problem-solving procedure. However, most real-world network optimisation problems (NOPs) are highly dynamic, where the network topologies, availability of resources, interference factors, user requirements, etc., are unpredictable, change with time, and/or are unknown a priori. This poses many difficulties for decision makers, generating significant optimisation challenges. This research aims to investigate Dynamic NOPs (DNOPs) in various network environments. The dynamics in both network environments and problems will be studied in depth. DNOPs occur across a wide range of application areas, such as communication networks, transport network, social networks, and financial networks. Our theoretical study in this project will seek fundamental insight that is applicable to multiple application areas, while our applied research will focus on railway networks and telecommunications networks. Evolutionary Computation (EC) encompasses many research areas, which applies ideas from nature (especially from biology) to solve optimisation and search problems. EC has been successfully applied to many real world scenarios, especially for difficult and challenging problems and those problems that are difficult to define precisely. This project aims to investigate EC methods for solving DNOPs. We aim to gain insight and further our understanding of how different EC methods can be applied to DNOPs via empirical and theoretical studies. It is important to carry out this research at both theoretical and empirical levels, as one can feed into the other. We will work with industrial partners (e.g., Rail Safety and Standards Board, and Network Rail) who will validate our research and participate in our project. We can utilise their skills and expertise in producing the underlying theoretical models, which can then be validated on real-world data supplied by them. This project has great potentials to fundamentally change the way in which DNOPs are treated, both from a real-world point of view and from the point of view of advancing our theoretical understanding. We plan to develop a prototype system, in collaboration with our industrial partners, for our industrial partners. In order to test and evaluate our newly developed algorithms for DNOPs, we will develop a set of common DNOP models that capture the real-world complexities, and develop advanced EC methods to solve these DNOP models. This will benefit wider research communities due to the ubiquity of DNOPs in so many different fields from communication networks to transport networks to social networks to financial networks. The research results of this project will also be of significant benefit to many industries that involve DNOPs and will provide significant savings both from a cost point of view as well as from an environmental perspective.

Flight Data Monitoring (FDM) is the process by which data from on-board recorders (or so-called 'black boxes') is subject to regular and systematic analysis, not just after emergencies but after every flight. This is performed so that subtle trends which arise as pre-cursors to more serious incidents can be detected in advance and used to proactively manage risk. The same technique is also used as a way of meeting environmental and economic goals through improved operational efficiency, fuel consumption and maintenance. FDM is relevant to the railway industry because since 2005 all trains now have to carry similar on-board recorders. The primary motivation is to provide accident investigators with an invaluable diagnostic tool, but like the aviation sector, because accidents are comparatively rare a far greater quantity of data is collected on normal, routine, non-accident journeys. As a result, recorder data in the rail industry represents a significantly underused resource. The proposed research relates to a class of problem which occurs firmly at the human/system interface and which is shared by both the aviation and rail sectors. Both domains experience problems, for example, Signals Passed At Danger (or SPADs) and Controlled Flight Into Terrain (CFIT), where several safety systems are defeated by human operators and otherwise fully functional trains or aircraft are placed in highly unsafe conditions. Problems such as these fall within the purview of Human Factors. On-board recorder data, be it from the rail or aviation sectors, represents a novel source of input for established human factors methodologies targeted at addressing them. The primary goal of the research, therefore, will be to couple recorder data to human factors methods in a way not previously attempted. The outcome will be 'leading indicators' of problems which, so far, have proven resistant to conventional safety interventions. Related to this are leading indicators, or metrics, that could help to inform ongoing research into operational efficiency, 'eco-driving', and potentially cost-saving insights into future maintenance practices. These opportunities can be systematically examined with reference both to human factors methods and to the mature FDM processes that currently exist in the aviation industry. The project is set against, and motivated by, a wider backdrop of European rail interoperability, a desire to maximise the environmental benefits of rail travel and the UK's position as a world leader in FDM processes. Whilst the research has at its core an innovative programme of theoretical advance, it is also coupled to several near-term applications. Firstly, the UK Civil Aviation Authority (CAA) seek to inform (and be informed of) best practice in other transport domains and the proposed project aims to provide a conduit for such knowledge. Secondly, both the CAA and the Association of Train Operating Companies (ATOC) are actively seeking leading indicators of safety related problems, particularly those which occur at the human/system interface. The project will map important theoretical developments in human factors methods to these real-world applications. Third, the proposed research is directly relevant to current industry projects managed by the Rail Safety and Standards Board (RSSB), including several relating to safety management systems, eco-driving and route knowledge. In summary, the proposed research represents a highly innovative approach to understanding and diagnosing issues which occur at the boundary of humans and transport systems. It is also an example of research with high economic and societal impacts, and an example of research application with great potential to develop further work and collaborations with industry partners.

This project looks across utilities at interdependencies and efficiencies of infrastructure at points of energy conversion. The hypothesis is that changes to these conversion points are critical to systemic sustainability and adaptability. But very few attempts have been made to consider how integrating or rebalancing (using less or more of) particular conversion points could make significant improvements to sustainability and adaptability of the system overall. This needs to be researched in the context of the user and the environment. The outcome of the current single utility approach is that power, water, transport etc are integrated at the point of use to create services demanded by end users. Services that help to shape sustainable behaviour are critical to the development of the infrastructure itself. The project will make use of existing research, such as that for energy use in the home, in order to ground our understanding of utility services needs. A fundamentally important aspect of national infrastructure is its technological context. Older infrastructure was built with a mechanical focus during a Taylorist machine paradigm. The global information (and network) age is upon us, with diverse opportunities in nanotechnology, hydrogen, battery storage, etc. In particular the ability to transmit information about utility operation and demand using telecommunications has been applied successfully to improve individual utility efficiency. The project will identify the latest relevant technologies and upcoming inventions and trends so that it frames proposals for change to the national utilities infrastructure in the current/future technological paradigm. Our methodology to deliver this research this is to 1. Create a map of current conversion points and so identify the critical parts of the national infrastructure, volumes, network structures, institution types, regulators, and so on. 2. Create a base-line agent based model to demonstrate the sustainability and adaptability of the current national utilities infrastructure, focusing on efficiency losses and resilience issues at conversion points. A system as large as the national infrastructure cannot be readily experimented upon and so modeling is one of few appropriate methods which have a successful track record adding insights not discernable by other methods since they are better at reflecting real world systems. The model will incorporate environmental factors, such as diminishing fossil fuels, services focus and adoption of appropriate technology, and so combine existing research. All aspects of the model will be guided by the expert knowledge of the team. 3. Run a workshop bringing together representatives from all utilities, including regulators, policy makers, major and minor firms, scientists and technologists, allowing expert opinions to be debated. The key outcome is a prioritized list of future scenarios which are service focused and in the context of technological possibilities. 4. The base-line model will be developed to include the future scenarios. The most favourable scenario (or two) which generates the greatest improvement in efficiency and resilience of the system, will be further developed to create a convincing quantified case for transforming utility conversion points. There will be an analysis over time of the changes to efficiencies as a result of the transformations, which will enable a business case to be developed and incorporate changes needed to existing legislation, regulation and policy. 5. A second workshop will review the results of futures model, allowing stakeholders to verify and challenge the results, and to plan for changes using the demonstrable recommendations for improvement to sustainability and adaptability. This project is complementary to other projects awarded funding in the Assets Sandpit, aligning with a services, integrated, technological and simplified view of national infrastructure.