The project will provide strategic insights regarding the integration of the transport sector into future low carbon electricity grids, and is inspired by limitations in current grid investment, operation and control practices as well as regulation and market operation, which may prevent an economically and environmentally effective transition to electric mobility. Although various individual aspects of the operation of electricity systems within an integrated transport sector have received some research attention, integrated planning of the grid, EV charging infrastructure and ICT (information and communication technologies) infrastructure design have not been addressed yet. In this proposal we propose to tackle these challenges in an integrated manner. At the heart of our proposal is a whole systems approach. It recognises the need to consider: EV demand and flexibility, electricity network operation and design, charging infrastructure operation and investment, ICT requirements and business models for electric mobility. This is essential when considering constraints imposed by the network on EV charging, and in return the requirements imposed by EVs on the system design and operation. This research will place emphasis on future energy scenarios relevant to the UK and China, but the tools, methods and technologies we develop will have wider applications. Specifically, a number of infrastructure planning related challenges for the massive rollout of EV have yet to be comprehensively investigated. First, traditional models of the travel of vehicles are based on the statistical prediction of aggregate-level travel demand without capturing the behavioural characterisation of users' driving requirements and preferences. Hence, this project will investigate new alternative activity-based travel demand models capturing in a bottom-up approach the behavioural basis of individual users' decisions regarding participation in activities yielding driving needs, behavioural aspects related to EV adoption and alternative EV charging strategies, as well as the characteristics of EV and the charging infrastructure. Unlike the existing models that analyse the EV impacts on isolated sectors of the power system, this project will assess economic effects on generation, transmission and distribution sectors simultaneously and subsequently reveal trade-offs between the cost and benefit streams of different EV charging strategies for different actors in the electricity chain. Furthermore, the closely related problem of EV charging infrastructure and ICT infrastructure planning -which has a central role in the massive EV rollout- has been almost completely neglected. This research project will examine novel risk-constrained stochastic optimization approaches in order to address the challenge of strategically investing in EV recharging and ICT infrastructures ahead of need, and will analytically investigate the interdependence between the power systems and EV enabling infrastructure planning. This project will also investigate alternative business models for the EV market integration and will propose a framework providing the opportunity for EVs to simultaneously support more efficient system operation and investment in assets across the entire electricity system chain. This research will formulate a new decentralised, market-based planning mechanism appropriate for deregulated power system environment and enable the investigation of the impact of alternative market designs and arrangements on the cost effectiveness of EV integration. Finally, a set of comprehensive use cases employing tools and methodologies developed in the project will be employed to understand the role and the importance of electric mobility in future UK and China low carbon systems and produce a suitable commercial and regulatory framework and a set of policy recommendations on ways of supporting the optimal deployment of EV infrastructure.

Traffic jams are an annoying feature of everyday life. They also hamper our economy: the CBI has estimated that delays due to road traffic congestion cost UK businesses up to 20 billion annually. UK road traffic is forecast to grow by 30% in the period 2000-2015, so it seems that the congestion problem can only get worse. There is consequently an intense international effort in using Information and Communication Technologies to manage traffic in order to alleviate congestion --- this broad area is known as Intelligent Transport Systems (ITS). Regular motorway drivers will already be familiar with ITS. Examples include 1. the Controlled Motorways project on the M25 London Orbital (which sets temporary reduced speed limits when the traffic gets heavy); 2. Active Traffic Management on Birmingham's M42 (where the hard-shoulder becomes an ordinary running lane in busy periods); and 3. The `Queue Ahead'warning signs which are now almost ubiquitous on the English motorway network. The investment in this telematics infrastructure has been very significant --- about 100 million pounds for Active Traffic Management alone.Each of the ITS applications described above has at its heart detailed mathematical and computer models that forecast how traffic flows and how queues build up and dissipate. However, these models are far from perfect, and the purpose of this research is to improve the models by working on the fundamental science that underpins them. This a so-called multiscale challenge, since there is a whole hierarchy of models of different levels of detail, ranging from simulation models that model the behaviour of individual drivers, up to macroscopic models that draw an analogy between the flow of traffic and compressible gas. This research will establish methods for finding out which models are good and which ones are bad. Moreover, it will use modern `machine learning' techniques to combine good models so that computer-based traffic forecasting has human-like artificial intelligence.

Infrastructure is a large part of the UK's assets. Efficient management and maintenance of infrastructure are vital to the economy and society. The application of emerging technologies to advanced health monitoring of existing critical infrastructure assets will quantify and define the extent of ageing and the consequent remaining design life of infrastructure, thereby reducing the risk of failure. Emerging technologies will also transform the industry through a whole-life approach to achieving sustainability in construction and infrastructure in an integrated way - design and commissioning, the construction process, exploitation and use, and eventual de-commissioning. Crucial elements of these emerging technologies will be the application of the latest sensor technologies, data management tools and manufacturing processes to the construction industry, both during infrastructure construction and throughout its design life. There will be a very substantial market for exploitation of these technologies by the construction industry, particularly contractors, specialist instrumentation companies and owners of infrastructure.In this proposal, we seek to create the Innovation and Knowledge Centre for Smart Infrastructure and Construction that will bring together four leading research groups in the Cambridge Engineering Department and the Computer Laboratory (sensors, computing, manufacturing engineering and civil engineering), along with staff in other faculties - the Judge Business School and the Department of Architecture. The Centre will develop and commercialise emerging technologies which will provide radical changes in the construction and management of infrastructure, leading to considerably enhanced efficiencies, economies and adaptability. We propose to create 'Smart Infrastructure' with the following attributes: (a) minimal disturbance and maximum efficiency during construction, (b) minimal maintenance for new infrastructure and optimum management of existing infrastructure, (c) minimal failures even during extreme events (fire, natural hazards, climate change), and (d) minimal waste materials at the end of the life cycle. The IKC will focus on the innovative use of emerging technologies in sensor and data management (e.g. fibre optics, MEMS, computer vision, power harvesting, Radio Frequency Identification (RFID), and Wireless Sensor Networks). These will be coupled with emerging best practice in the form of the latest manufacturing and supply chain management approaches applied to construction and infrastructure (e.g. smart building components for life-cycle adaptive design, innovative manufacturing processes, integrated supply chain management, and smart management processes from building to city scales). It will aim to develop completely new markets and achieve breakthroughs in performance.The business opportunities in construction and infrastructure are very considerable, not only for construction companies but also for other industries such as IT, electronics and materials. The IKC is designed to respond directly and systematically to the input received from industry partners on what is required to address this issue. Through the close involvement of industry in technical development as well as in demonstrations in real construction projects, the commercialisation activities of emerging technologies will be progressed during the project to a point where they can be licensed to industry. The outputs of the IKC will provide the construction industry, infrastructure owners and operators with the means to ensure that very challenging new performance targets can be met. Furthermore the potential breakthroughs will make the industry more efficient and hence more profitable. They will also give UK companies a competitive advantage in the increasingly global construction market.

The resilience of building and civil engineering structures is typically associated with the design of individual elements such that they have sufficient capacity or potential to react in an appropriate manner to adverse events. Traditionally this has been achieved by using 'robust' design procedures that focus on defining safety factors for individual adverse events and providing redundancy. As such, construction materials are designed to meet a prescribed specification; material degradation is viewed as inevitable and mitigation necessitates expensive maintenance regimes; ~£40 billion/year is spent in the UK on repair and maintenance of existing, mainly concrete, structures and ~$2.2 trillion/year is needed in the US to restore its infrastructure to good condition (grade B). More recently, based on a better understanding and knowledge of microbiological systems, materials that have the ability to adapt and respond to their environment have been developed. This fundamental change has the potential to facilitate the creation of a wide range of 'smart' materials and intelligent structures. This will include both autogenous and autonomic self-healing materials and adaptable, self-sensing and self-repairing structures. These materials can transform our infrastructure by embedding resilience in the components of these structures so that rather than being defined by individual events, they can evolve over their lifespan. To be truly self-healing, the material components will need to act synergistically over the range of time and length scales at which different forms of damage occur. Conglomerate materials, which comprise the majority of our infrastructure and built environment, form the focus of the proposed project. While current isolated international pockets of research activities on self-healing materials are on-going, most advances have been in other material fields and many have focussed on individual techniques and hence have only provided a partial solution to the inherent multi-dimensional nature of damage specific to construction materials with limited flexibility and multi-functionality. This proposal seeks to develop a multi-faceted self-healing approach that will be applicable to a wide range of conglomerates and their respective damage mechanisms. This proposal brings together a consortium of 11 academics from the Universities of Cardiff, Bath and Cambridge with the relevant skills and experience in structural and geotechnical engineering, materials chemistry, biology and materials science to develop and test the envisioned class of materials. The proposed work leverages on ground-breaking developments in these sciences in other sectors such as the pharmaceutical, medical and polymer composite industries. The technologies that are proposed are microbioloical and chemical healing at the micro- and meso-scale and crack control and prevention at the macro scale. This will be achieved through 4 work packages, three of which target the healing at the individual scales (micro/meso/macro) and the fourth which addresses the integration of the individual systems, their compatibility and methods of achieving healing of recurrent damage. This will then culminate in a number of field-trials in partnership with the project industrial collaborators to take this innovation closer to commercialisation. An integral part of this project will be the knowledge transfer activities and collaboration with other research centres throughout the world. This will ensure that the research is at the forefront of the global pursuit for intelligent infrastructure and will ensure that maximum impact is achieved. One of the primary outputs of the project will be the formation and establishment of a UK Virtual Centre of Excellence in Intelligent Construction Materials that will provide a national and international platform for facilitating dialogue and collaboration to enhance the global knowledge economy.

We are living in an unprecedented age where vast quantities of our personal data are continually recorded and analysed, for example, our travel patterns, shopping habits and fitness routines. Our daily lives are now tied into this evolving loop of data collection, leading to data-based automated decisions, that can make recommendations and optimise our routines. There is tremendous economic and societal value in understanding this deluge of unstructured disparate data streams. A key challenge in Artificial Intelligence (AI) research is to extract meaningful value from these data sources to make decisions that can be trusted and understood to improve society. The PASCAL research programme is focused on developing an end-to-end framework, from data to decisions, that naturally accounts for data uncertainty and provides transparent and interpretable decision-making tools. The algorithms developed throughout this research project will be generally-applicable in a wide range of application domains and appropriate for modern computer hardware infrastructure. All of the research and associated algorithms will be widely available through high-quality open-source software that will ensure the widest possible uptake of this research within the international AI research community. PASCAL will focus on two primary applications areas: cybersecurity and transportation, which will stimulate and motivate this research and ensure wide-spread impact within these sectors. To drive through the impact and uptake of this research within these sectors, we will work closely with committed strategic partners, GCHQ, the Heilbronn Institute of Mathematical Research, Transport Research Laboratory, the University of Washington and the Alan Turing Institute. Cybersecurity - The proliferation of computers and mobile technology over the last few decades has led to an exponential increase in recorded data. Much of this data is personally, economically and nationally sensitive and protecting it is a key priority for any government or large organisation. Threats to data security exist on a global scale and identifying potential threats requires cybersecurity experts to evaluate and extract critical intelligence from complex and evolving data sources. In order to model and understand the intricate patterns between these data sources requires complex mathematical models. The PASCAL programme will develop new algorithms that maintain the richness of these mathematical models and use them to provide interpretable and transparent decision recommendations. Autonomous vehicles (AV) - The transition to AVs will be the most significant global change in transportation for the past century. The economic benefit and successful implementation of this technology within the UK requires a thorough understanding of the risks posed by driverless vehicles and what new procedures are required to ensure human safety. Through PASCAL, we will develop a framework to artificially-generate realistic traffic scenarios to test AVs under a wide range of road conditions and create criteria to safely accredit AV vehicles in the UK.