Cities face a number of key transport challenges in the 21st Century. Congestion has a set of well-documented negative consequences, including environmental pollution, poor economic competitiveness, low levels of public satisfaction with public transport, negative impacts on personal health and wellbeing, and a broader reputational impact on urban centres that aspire to be retail and tourist destinations. Moreover, the UK's commitment to reduce carbon emissions necessitates a broader long-term shift away from private motor transport towards low carbon and mass transit modes of transport. In this way, tackling the specific issue of urban congestion relates to wider social and economic goals for cities to become better places to live and easier places to navigate. Specifically, Exeter has some of the worst air pollution and congestion statistics for a city of its size. Yet the city is also witnessing unprecedented expansion of outlying suburbs, creating greater pressure on existing arterial and city centre roads. Alongside these background factors, the city aspires to maintain its position as a key retail and tourist centre and there are plans to redevelop several areas of the city centre for retail and leisure facilities in the coming years. One of the ways in which social scientists have attempted to deal with this 'wicked' policy problem is to promote pre-formed behavioural change through the provision of information and exhortations to individual travellers to change their behaviour. However, decades of social research has illustrated that influencing behaviour is highly complex and requires a significant investment in research intelligence about what influences travel behaviours. Although recent years have witnessed a growth in social marketing approaches for influencing change, which adopt the methods of conventional marketing approaches, a set of factors that have been largely omitted from such studies and interventions is the role of what can be termed 'real time' factors in travel decision making, which are not pre-formed, but which influence practices in the moment. Research intelligence from traffic management providers suggests that factors such as weather conditions, immediate levels of traffic congestion and perceptions of the effectiveness of public transport are all important to consider when understanding both decisions to travel and also the resultant behaviour of travellers on their journeys (either as drivers or public transport users). Indeed, crucial to understand are the ways in which these conditions can be communicated to promote different practices, either as decisions to travel using different modes or to drive in a different way, and the potential for harnessing new technologies for managing travel behaviour through both the utilisation of sophisticated traffic management systems. This research therefore aims to understand and promote better 'real time' travel decision making through adopting a personalised and tailored travel behaviour approach. This will be undertaken through a two stage methodology. In stage 1, a large general survey of Exeter residents and those travelling into Exeter on a daily basis will be undertaken to explore key travel behaviours, attitudes, participants' characteristics. Using an online survey approach, the questionnaire will enable researchers to both identify key segment groups and their travel behaviours and, on the basis of these analyses, to make high level statistical links between individual behaviours and external factors, including quantitative information from other datasets. In stage 2, a panel of representative participants from the segments identified at stage 1 will be formed to explore the key relationships between behaviours and specific interventions that will be captured through a series of experiments, which will test interventions and their effectiveness.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.