Public Funding for Block Dox Limited

**TRUST2** is an industrial research project that aims to accelerate the adoption of Artificial Intelligence (AI) in building management. Despite its potential impact, decision makers in the building industry are hesitant to make the investment required and adopt AI due to a lack of trust in its effectiveness and reliability. Managers and owners need to see convincing real-world demonstrations of AI/ML systems saving money and energy, keeping operations efficient, people comfortable and safe, in similar buildings to their own. System integrators also do not want to risk their reputation by recommending unproven technologies to their customers. Despite the risks, the potential payback for investment in AI in the built environment is significant. With rising energy prices, businesses are now increasingly concerned about controlling energy costs beyond sustainability reasons. Other operational efficiencies are also achievable, from space utilisation to manpower. **TRUST2** aims to demonstrate the benefits of AI in building management, particularly in the United Kingdom where the smart building industry lags behind in adoption despite being a leader in AI/ML innovation. This Phase 2 project utilises insights from sensor and other data with AI and machine learning as the expert in the loop to control selected building management systems. The Phase 2 real-world application of these technologies in an existing building will be evaluated and highlighted in live demonstrations, case studies, scientific papers, and articles to increase trust in the use of AI in building management and reduce barriers to adoption in the industry.

**TRUST** is a feasibility study that aims to accelerate the adoption of Artificial Intelligence (AI) in building management. Despite its potential impact, decision makers in the building industry are hesitant to adopt AI due to a lack of trust in its effectiveness and reliability. This is particularly true in buildings where failures could have dire consequences, such as hospitals, banks and schools. These types of buildings are typically larger and offer even greater benefits if the AI were to work effectively. The primary concern for decision makers is the lack of guarantees for reliability and return on investment. Without these guarantees, decision makers are not willing to enable automation by integrating often disparate technologies, or fitting new building management systems (BMS) at significant upfront cost. System integrators also do not want to risk their reputation by recommending unproven technologies to their customers. Despite the risks, the potential payback for investment in AI in the built environment is significant. With rising energy prices, businesses are now increasingly concerned about controlling energy costs beyond sustainability reasons. Other operational efficiencies are also achievable, from space utilisation to manpower. **TRUST** is a research and development initiative that aims to demonstrate the benefits of AI in building management, particularly in the United Kingdom where the smart building industry lags behind in adoption despite being a leader in AI/ML innovation. This Phase 1 feasibility study will establish a consortium and develop a system that in Phase 2 utilises insights from sensor and other data with AI and machine learning as the expert in the loop to control selected building management systems. The Phase 2 real-world application of these technologies in an existing building will be evaluated and highlighted in live demonstrations, case studies, scientific papers, and articles to increase trust in the use of AI in building management and reduce barriers to adoption in the industry.

**BlockDox** is a data science focused proptech company. **BlockDox's** intelligent platform brings meaning to data as real-time, predictive and actionable insights. This Global Feasibility Study project will explore smart city opportunities in the Kingdom of Saudi Arabia. The Kingdom of Saudi Arabia (KSA) is investing heavily in large scale smart city "giga" projects and related digital technologies as part of its Vision 2030 programme. Some of the world's largest and most ambitious smart city projects are being planned in KSA including Neom (the largest civil project in the world currently under construction), and Amaala. BlockDox are particularly interested in this emerging and exciting opportunity in KSA due to its strong alignment with their technological solution, project size and significant reputational value

"Indoor environmental air quality directly impacts the health, wellbeing, & productivity of occupants. Studies evidence 4-6% performance decrease when offices are too hot/too cold; 101% increase in cognitive scores in well-ventilated offices and increased in productivity per employee of at least US$6,500\. Public Health England blames unhealthy buildings for costing the NHS over £7.4bn annually, & research from the Canadian Green Building Council has shown healthy buildings to be worth at least 7% more. The UK Green Building Council has identified current methods for measuring indoor environment data as ""_highly challenging_"". **INSENS** is an affordable, retrofitable, real-time Indoor Environmental Sensor analytics solution to measure & analyse building health in real-time, integrated with real-time occupancy data & building management systems (BMS). The solution addresses an unmet market challenge with potential to be applied to other sectors including Smart Home & Smart Transportation."

China's economic development and accelerated urbanisation exposes cities to severe challenges, particularly a scarcity of resources including water, gas, and electricity. The use of natural resources by a city also has an impact on global warming. Buildings are the gas guzzlers of the property world. 40% of carbon emissions come from the way buildings are heated/cooled, lit and used. Most buildings are managed today based on fixed assumptions, rather than real time demand. This is an inefficient way to manage the built environment with severe implications for energy efficiency and the demand placed on natural resources. This project brings together UK and China commercial and academic partners who will develop a tool that can be used to understand the demand placed on natural resource consumption by a city, and future interventions that could be applied in response. This will be based on a spatial mapping platform enhanced with real time occupancy data from innovative sensing technology in buildings. With an emphasis on electricity, it is anticipated that a positive impact will be identified in the pilot location better aligning actual demand with resource availability and highlighting the consequential opportunities for improving cost efficiencies. Broader implications include reducing carbon emissions and positively impacting the UN sustainability goals including Goal 7 (Affordable and Clean Energy) and Goal 11 (Sustainable Cities and Communities). These benefits should be applicable across other cities in China, with particular relevance to the 19 mega-city clusters planned to maintain their economic growth whilst mitigating any environmental impact. The technology could also apply to other cities in developing nations globally, in addition to those in developed nations such as the UK.

**CONTEXT**: Understanding realtime passenger rail service use is crucial, because overcrowded rail services (annually published as Passengers in excess of capacity, with maximum allowable PiXC of 4.5%) lead to more delays, increased risk of accidents, reduced passenger comfort (highest load measured was 184%, DFT 2012) and decreased passenger satisfaction (National Rail Passenger Survey (NPRS); Transport Focus, 2014) requiring train operators to invest in remedial measures, whilst underutilised services are not cost effective. Knowledge of passenger loading diversity by rail operators is not comprehensive, so services are provided on data collected: at route or network-level not station-level (insufficiently disaggregated); in discrete time episodes rather than continuously (incomplete); based on studies undertaken several years previously (out of date). Historically, the difficulties and expense of collecting and analysing high volumes of good quality data made such management decisions understandable. Recent developments in sensor and information communication technologies mean they are not justifiable today. **NEED**: COUNTER has implications for all types of routine, incidental and disrupted rail travel: crowding penalties (MOIRA2; PiXC), delays (Public Performance Measure), optimising boarding/alighting times, journey suppression, insufficient capacity, customer satisfaction, on-board experience, revenues, congestion, timetabling, train/station design, operational cost reduction, health and safety, pollution, energy saving and facilities management. Accurate intelligence about passenger flows/demand is critical for Intelligent Trains. Despite being the primary challenge facing all train operators obtaining a precise and reliable measurement of real time passenger demand remains difficult based on current techniques, e.g. video imaging/weight sensors. Impact: train demand inefficiently managed. **INNOVATION**: COUNTER offers a platform interoperable with existing Train Management Systems, combining a patent-pending sensor fusion method using wifi fingerprinting and lo-fi infrared sensors with machine learning algorithms to deliver an accurate assessment of real time and predictive passenger counting/flow. **OUTCOME**: COUNTER will generate predicted revenue of £118M within 5 years of launch, along with new jobs and generation of new knowledge with wider applications including all mass transit systems and building management. UK-wide, it will cut delays due to boarding and alighting by 10% (worth ~£5.5m per annum); reduce station and platform accidents by 10% -- i.e. ~ 700 passenger injuries a year; cut rail customer dissatisfaction levels by 10%; attract 0.8% more passengers to UK rail network from higher service quality -- i.e. £75m per revenue per annum; increase revenue from station/platform and train advertising by better qualifying eyeballs/impressions and dynamically trade advertising space (worth ~£160m per annum). **Keywords**: _Sensors, Footfall, Network Capacity_.

Rail passenger numbers in the UK doubled between 1996 and 2016 (currently 1.7billion p.a.) whilst the supply of train kilometres rose by ~50%, with implications for overcrowding on services and associated impacts on train delays, passenger comfort, safety and passenger satisfaction. Thus, 9.1% of rail services are delayed by incidents at stations of which 40% are due to alighting and boarding passengers at an annual societal cost of ~£55m per year. Meanwhile there were 6,866 passenger injuries on the UK rail network in the year to September 2016, of which 50% were due to slips, trips and falls, 21% were platform edge incidents and 9% were due to contact with object or person. Finally, 20% of passengers complain about insufficient sitting/standing room, whilst 24% are dissatisfied with space for luggage and 40% are dissatisfied with facilities such as toilets - both related to higher usage on busier services. TRAFFIC will address these issues by modifying sensors that detect people in buildings for use in the rail environment, and use them to continuously, cheaply and accurately monitor how people use trains. This is important because such data could be used to accurately assess and predict passenger flows, provide more space on trains and a personalised customer experience. This in turn would enable: train and station staff to dynamically and proactively manage crowding situations; train planning staff to devise more realistic railway timetables; train and station maintenance teams to better plan both say-to-day and longer-term maintenance schedules; and train buyers and manufacturers to better specify new trains and underpin future passenger guidance systems (e.g. through a smartphone app). In these ways, TRAFFIC will support the UK Department for Transport's vision "to make journeys better: simpler, faster and more reliable" whilst supporting jobs, enabling business growth, and bringing the UK closer together.

With implications for customer satisfaction, revenue performance, congestion, route & space planning, operational cost reduction, health & safety, pollution, energy saving & facilities management, having accurate knowledge of the localised demand from passengers is critical data to smart intelligent transport strategies. Passenger demand data underpins the 4 core objectives of the DfT and the Gvt’s vision “to make journeys better: simpler, faster and more reliable” whilst supporting jobs, enabling business growth, and bring the UK closer together. BlockDox offers an interoperable platform that can be integrated into existing Transport Management Systems. It combines a patent-pending sensor fusion method with artificial intelligence, unique machine & deep learning algorithms to deliver an accurate assessment of real time & predictive passenger counting/flow. The solution addresses an unmet market challenge with 99% accuracy with the potential to deliver improved operational efficiency, journeys, improved security implications incl. crisis management. Potential for the solution to be applied to other sectors including building/facilities management, retail and event sectors.

Awaiting Public Project Summary

With implications for space optimisation, operational cost reduction, H&S, & energy performance (buildings accounting for ~40% of total energy use, with a significant part of this energy wasted in servicing unoccupied buildings), having accurate knowledge of localised occupancy information is critical to smart intelligent building strategies. Despite representing the primary challenge facing all building operators & facility managers obtaining a precise and reliable measurement of occupancy remains difficult based on current solutions, the impact being that most buildings are inefficiently managed with poor energy performance. BlockDox offers an interoperable platform that can be integrated into any Building Management System. It combines a patent-pending sensor fusion method with unique machine & deep learning algorithms to deliver an accurate assessment of real time & predictive people counting/flow. The solution addresses an unmet market challenge with 99% accuracy with the potential to deliver up to 56% Heating, Ventilation, and Air conditioning (HVAC) savings, improved staff resourcing/use of floor space, improved security implications incl. crisis management. Potential for the solution to be applied to other sectors including Transport, hospitality & healthcare

With implications for space optimisation, operational cost reduction, H&S, & energy performance (buildings accounting for ~40% of total energy use, with a significant part of this energy wasted in servicing unoccupied buildings), having accurate knowledge of localised occupancy information is critical to smart intelligent building strategies. Despite representing the primary challenge facing all building operators & facility managers obtaining a precise and reliable measurement of occupancy remains difficult based on current solutions, the impact being that most buildings are inefficiently managed with poor energy performance. BlockDox offers an interoperable platform that can be integrated into any Building Management System. It combines a patent-pending sensor fusion method with unique machine & deep learning algorithms to deliver an accurate assessment of real time & predictive people counting/flow. The solution addresses an unmet market challenge with 99% accuracy with the potential to deliver up to 56% Heating, Ventilation, and Air conditioning (HVAC) savings, improved staff resourcing/use of floor space, improved security implications incl. crisis management. Potential for the solution to be applied to other sectors including Transport, hospitality & healthcare.

BlockDox addresses the significant business opportunity & demand that exists by providing a platform for enhancing building management with real time and predictive intelligence. Their solution, where strong interest has already been shown from the property market, combines an interoperable platform customised specifically to an individual building with a patent-pending method using geofencing, sensors & beacons to deliver an accurate assessment of building occupancy & use of communal spaces. This project aims to explore the significant opportunity for adapting BlockDox technology for use outside, thereby opening a new international market for their solution. By collaborating with Berlin based Green City Solutions, the feasibility and commercialisation of BlockDox’s solution in outdoor environments can be accelerated with particular focus on tackling the major problem of air pollution in cities worldwide. If successful, there is further potential for deployment in the global market for public and private outdoor space management, including large events and festivals.

We would like assistance from an external UX consultant for our proprietary building management software.