The project objective is to develop a commercially viable innovative future transport authority facing integrated toolset called ICEIMO (Intermodal Capacity, Environmental and Intelligent Mobility Optimisation). ICEIMO will create a truly demand-responsive intermodal transport network and demonstrate this concept at a major London transport hub. This will create and utilise innovative transport demand data filling existing data gaps, novel predictive transport demand algorithms, Intelligent Mobility impact toolsets and new field test service to monitor air quality. Transport infrastructure will increase its resilience to population growth through making operational decisions on intermodal capacity to dynamically allocate capacity based on demand and air quality drivers, realise business efficiencies and reduce operating costs (proving viability). The consortium will approach the project by applying specialist skills and knowledge to the project. This improves current state-of-the-art tools because new real-time modelling software/datasets, air quality and Intelligent Mobility impacts will be incorporated into operational tools, refined and validated through industrial research field trials. A substantial impact on the market, partner products and strategies is expected in the next 3-5 years.

Asset owners require reliable & long-term monitoring and assessment of their asset performance and condition so that they can schedule maintenance and ensure the maximum utilisation and life of their assets. Key to this is the application of structural health monitoring (SHM) techniques providing increased accuracy over existing survey methods. A variety of in-situ sensing techniques are used to assess the health, such as accelerometers, strain gauges and displacement sensors. This project intends to develop the tools necessary to allow a satellite-SHM product and service to be offered. Using satellite remote sensing (principally InSAR measurements of displacement) to inform structural health will allow such assessments to be made for a multitude of assets since satellites can image many thousands of km2 in one pass, contributing to a lower cost per asset and application to assets otherwise not frequently assessed. TVUK will act as satellite data provider and lead, TWI as expert in SHM provision, ThinkLab as expert in asset 3D modelling and BIM and STLTech as SHM data provider. A new product utilising InSAR data with structural health modelling and 3D visualisation will be produced, with proof of concepts with Transport for London (also a partner) and EDF.

McLaren's Applied Technologies motorsport telemetry and data analysis capabilities gives their Formula One racing teams a key competitive advantage during races and post event review whilst OpenCapacity's industry leading transport data processing, aggregation and machine to machine interfaces enables transport operators and the public to exploit the value of data in new ways. Both of these products lead their respective fields in meaningful data collection, analysis and visualisation. The particular demands of Formula One including real time, high data volumes and continually verifying modifications made to vehicles and continually seeking new marginal gain improvements matches well to the problems that public transport authorities faced when viewed in a cities context. Given that cities face increasing societal and environmental pressures, the marginal gains approach of finding, modelling and verifying these improvements in real time with real data to deliver a competitive edge can be applied to the public transport network to better manage these pressures. In the first phase, this project will seek to firstly confirm feasibility and develop a repeatable robust business case for the deployment of this innovation both as a product offering and also the internal business case for the customer as an end user. The second phase will deploy this innovation in London as a real life challenge via Transport for London, supported by OpenCapacity's transport data expertise, handling capabilities and machine learning platforms to demonstrate and quantify real life benefits. The case studies that will be put through the deployed solution will be a mixture of existing pipelined improvements to infrastructure and vehicles to better verify and manage their citywide benefits such as air quality/energy consumption and new opportunities identified by the data visualisation tools supported by McLaren’s historical insight.

This study will look to determine the feasibility of transferring waste heat from London Underground to Islington Borough Council’s district heating network. As part of an upgrade to the network, additional heat will be produced by the increase in train frequency. To mitigate the rise in heat and reduce the risk or heat strain to passengers, a new cooling system is to be installed. An opportunity has been identified to utilise the heat exchanger at York Road to support the expansion of Islington Borough Council’s district heating network. The feasibility study will investigate the technical viability and business case of utilising London Underground’s heat exchanger at York Road to transfer heat to Islington Borough Council’s district heating network. Combining the two systems would reduce the energy required by both parties.

London Underground is tasked with increasing the sustainability of its’ major infrasructure projects, including both the construction, operation and maintenance. Continued London population and employment growth and associated increase in passenger demand necessitates continued major station capacity upgrade projects. Holborn station is LU’s highest priority station capacity upgrade project and will be delivered by the mid 2020s. The project will also facilitate station platform cooling and a replacement, expanded basement level traction sub-station to support the planned Central line upgrade increased train frequency over the next 10-15 years. Combined these two project elements will provide waste heat that would otherwise be exhausted to atmsophere, creating an unsustainable outcome for London. The Trigeneration feasibility study therefore aims to test whether this waste heat could be harnessed to supply local third parties and to test whether there is a more holistic, decentralised means of generating cooling and power as part of the station capacity project.y

Customer experience is enhanced it two ways, either improving services or resolving problems well. Either case cannot be achieved without engaging customers in innovation. However, for each customer-facing organisation (CFO) the cost and risks of building the necessary IT innovation infrastructure, individually, are considerable barriers. Worse still, such fragmentation is confusing for customers. The project will address these challenges by developing a single UK-wide innovation infrastructure seamlessly connecting customers to any CFO and their supply chain. The project will pilot the infrastructure on a range of CFO projects such as addressing the needs of the visually impaired passenger, better “wayfinding” at stations/platforms, reduction of disruption due to suicide attempts, innovative group ticketing e.g. families, school outings. They also want to develop customer experience enablers and digital assets including social media analysis, journey mapping, digital customer panels, extracting more insights from passenger surveys all of which will allow the industry to achieve a step- change in its ability to improve customer experience through innovation.

The project will develop a customer facing UI solution called “Accelerate” that will utilise the high density of passenger mobile devices and the observational powers of their owners on the LU rail network. The vision is use a range of actively and passively collected data to enable 2-way interactions between train passengers and TOC’s maintenance systems using privacy best practices. The dialogue enabled will allow reporting of maintenance issues as they are spotted and instant feedback on resolution progress. The project will enable future TOC operating efficiencies to be realised (proving long term commercial viability) and added value to SME existing transport product offerings. The mobile solution will captures and analyse customer motion and future health data readings to help network performance and maintenance activities with previously- unavailable live data. There will also be new ultra-personalised travel recommendations to increase customer satisfaction and new commercial products that arise from this motion source data.

When you board a train today it is noticeable that travellers are occupied with their connected devices in some way. What if their interaction included instant, useful and personalised travel information about their journey, whilst helping the travel operator to gain valuable personalised feedback data at the same time? In this project a unique platform will be designed, created and tested that helps a user to customise their travel experience based on large-scale data analysis of real-time information from data feeds, end user community contribution, transport systems and sensors.

PCIPP (A People Centred Approach to Intelligent, Proactive, Predictive Asset Management) will advance a state-of-the-art Enterprise Wide (Any asset type, Any sensor type, Any Manufacturer), Enterprise Class (Robust, Flexible, User Friendly, Scalable, Future Proof) solution for intelligent asset management that goes beyond conventional Remote Condition Monitoring (RCM) and existing systems. PCIPP will eliminate the need for asset managers to maintain multiple vertically integrated RCM systems, reduce user training needs by providing a common interface for all assets and unlock the potential of true intelligence by fusing and correlating data across multiple assets and legacy systems to create actionable, prognostic information. PCIPP builds on the highly successful solution Thales provides to Network Rail in their Intelligent Infrastructure programme which monitors over 22,000 assets and has removed the need for 15,000 site visits since it went live in 2009. PCIPP is structured around a human-centric design process, using capabilities from world-recognised human factors experts, ensuring that operators have access to relevant information, despite the increasing amount of data available within PCIPP. PCIPP’s open architecture will enable an ecosystem to develop that will expand the range of assets covered; incorporate train, track and station data; integrate to maintenance systems; substantially increase diagnosis and prognosis capabilities and nurture a new market for the incorporation of 3rd party analytics modules.

The objective of this project is to use Remote Condition Monitoring (RCM) data to provide a reliable and dependable health assessment of the asset, to manage asset degradation and undertake maintenance intervention at the optimum time, in advance of failure. The project will provide an open architecture system that integrates data from a number of RCM sources. Condition Indicators will be derived from the RCM data based on detection of incipient defects and trends to develop an automated approach to introducing prognostics assessment via a risk-based Remaining Useful Life (RUL). This approach will significantly improve on current state detection methods which are based on simple thresholds. The technology developed will assess the RUL via a dynamic scheduler to determine the optimum maintenance period in order to minimise the risk of failure to the asset and maximise its availability. The project deliverable is to provide the end user with advisories (actionable information) relevant to their needs. This will ensure that ‘information overload’ is minimised and addresses security of information issues by only displaying information relevant to the rank and role of the user. The project will also address the process re-engineering and human factor issues resulting from the paradigm shift of moving from a schedule and demand based maintenance management regime to a condition based forecasting approach where static schedules and depth of maintenance regimes are replaced with dynamic processes.

This project aims to develop a commercially viable, lightweight, rail carriage door using state-of-the-art composite materials and manufacturing processes as a first step towards achieving the goal of introducing light-weight trains through technology innovation from the aerospace industry.

Train borne inspection of railway track has until recently been restricted to Ultrasonic or Electromagnetic testing and measurement, neither of which is capable of resolving shallow Rolling Contact Fatigue (RCF) cracks of a few mm long. Hence, these techniques are reactive to events & are not advanced enough to measure the key characteristics of RCF, i.e. shape, angle, length, linear density & position of crack initiation. They cannot be used to either enhance rail life or predict /prevent broken rails. It is this type of defect which was evidenced at Hatfield and is currently a subjective measurement resulting from manual visual inspection. This proposal details the development of an image acquisiton & analysis system enhanced by laser illumination & video imaging of the critical rail-wheel interface, in particular the contact band and the characterisation of visible rail head defects. This will be a preventative measure offering both safety and cost benefits. Image acquisition at high-speed, i.e. up to 125mph is a considerable challenge, requiring significant expertise. This is offered by the proposed consortium, along with ~8 years of rail defect data already obtained by Corus.