Public Funding for Prospective Labs Ltd

Public description Co-Operate is a flexible Bus Priority System that is delivered directly through the navigation software of individual vehicles and is designed to enable Local Authorities and transit operators to better manage the flow of buses, emergency service vehicles, public service vehicles, taxis and delivery vehicles (that all have access to bus lanes) in a manner that ensures an unobstructed path for emergency response and daily transit services. The system can be deployed without the need for expensive fixed infrastructure investment, enabling over and under capacity on the bus / road network to be managed more precisely, more flexibly and at a lower cost. Our feasibility study will engage public sector organisations and fleet operators in the Sheffield and Rotherham area. Our proposed trial will demonstrate the technology by controlling which vehicles can use certain routes within a defined control zone and provide routing and dispatch instructions to those vehicles. Initially this would be through a combination of voluntary access restriction and incentivisation and involve a minimum two vehicle fleets. The engagement will begin at a strategic level, to ensure political support and strategic alignment with public sector objectives, before engaging directly with the fleet (including bus operators and other fleets wishing to use bus infrastructure) and infrastructure operators to develop requirements. The feasibility study outputs will be used to validate the proposed use case, gather requirements and plan for the proof concept trial.

Social distancing measures have reduced the effective capacity of buses to 40% of regular capacity. Although many routes are not currently busy enough to be commercially viable overall, they can be too busy at certain times and passengers are being turned away as a result. With passengers already nervous about using public transport - such instances may prevent passengers from using the bus in the future. Operators must find ways to build confidence in bus services through improved passenger information that is reliable and constantly updated. Specifically, there is a need to provide passengers with information on the space available on buses, so passengers know if they will be able to board and if they will be comfortable with the experience. This information must go beyond advance planning tools, and provide real time, continuously updated predictions of the space on specific buses at specific stops. Our proposal will provide a real time data feed of expected arrival time and predicted passenger space for every vehicle and every bus stop on a bus route. This goes beyond existing solutions by combining real time occupancy sensing with real time prediction of boarding and alighting further down the route. The feed will be continuously updated and can be integrated into a wider range of passenger facing services, including passengers apps, digital bus stop signage and journey planning tools.

FlowOS is a Simulation/Machine Learning driven dispatcher for scheduled bus services. The FlowOS dispatcher enables bus operators to issue automated real-time instructions to drivers that are designed to regulate vehicle occupancy and maximise the use of a vehicle's capacity along a route. FlowOS sends automated instructions to the driver of each vehicle in a fleet, in response to live occupancy data, the current location of each vehicle and the forecast number of passengers wanting to board at each stop.

The bus industry was hit significantly by the COVID-19 crisis, with passenger numbers down by around 90%. Throughout the crisis bus companies have continued to provide a vital service, with running 40-50% of buses. To limit the financial impact on the industry a government support package has been agreed that will cover losses as long as services are maintained at a certain level and social distancing is enabled by limiting occupancy to 25-50% of vehicle capacity. Basic methods for monitoring occupancy have begun to be put in place by operators. For example some operators now require bus drivers to manually record every boarding and alighting passenger and prevent additional boardings if the occupancy limit is reached. While present demand means that the limit is rarely reached, as restrictions ease and use of more people begin to travel, operators must find ways to satisfy demand with a reduced capacity. Without new tools and approaches it will become increasingly common for passengers to be prevented from boarding ‘full’ buses. This could have serious implications for the attractiveness of bus travel and push people towards less sustainable forms of transport. To avoid this situation operators must ensure reliable and equitable access to services by adjusting services responsively. FlowOS Bus Occupancy Estimation and Management We will develop our existing FlowOS software to provide tools to manage occupancy by predicting how occupancy over the full bus route and adjusting calling patterns to ensure passengers waiting further down the route will still be able to board. For example, drivers on some services will be asked to ‘skip stops’ before they are ‘full’ if it is predicted that passengers waiting downstream would otherwise be unable to board services. This could reduce the average wait time of passengers at all stops and share the impact of reduced capacity along the route. Crucially however, this would only happen in response to increasing ridership and conditions on certain days, when passenger demand is likely to be higher. Ultimately this will: * Enable operators to better align supply with demand; * Ensure occupancy remains below government maximums; * Improve service performance and safety during the recovery; * Build passenger confidence in public transport. FlowOS uses simulation technologies to predict how transport networks will evolve in the near future and optimise schedules and operational instructions to maintain and improve performance in response to changing conditions. Our proposed extension will extend the potential impact of the project by carrying out market engagement to investigate the value and deployment of FlowOS occupancy estimation and management beyond the initial use case. This will include working with potential clients and other beneficiaries to evaluate the potential of demand prediction from historic demand data to predict use levels of bus stations, multimodal transport hubs, trains, micro-mobility rental, retail and delivery - and redistribute capacity and resources in response. The potential impact would be to enable these services to continue to operate efficiently during the COVID-19 pandemic. We also believe that the outcomes of this type of occupancy and congestion management will be an expected part of user experience management in the 'new normal' and so the impact goes well beyond the COVID-19 context. Prospective The solution will be developed by Prospective, a team of transport planners, data scientists and software engineers from Cambridge University and UCL's Centre for Advanced Spatial Analytics (CASA). We founded the company in 2016 and apply data science, simulation and modelling to real world mobility challenges.

"Infrastructure Ahead" will revolutionise the way infrastructure providers, utility supplers and planning authorities forecast and respond to end-use demand. Our Decision Support System (DSS) will forecast end-use demand dynamics at unpresedented spatial and temporal resolutions. The system relies on a large number of very detailed datasets, fused into a comprehensive representation of the built environment, its regulatory framework and its development potential. The embeded forecasting capabilities are driven by seamless integration of microeconomic modelling and machine learning. The system will be able to estimate current and short- and medium-term end-use demands, at the building level, under variable macroeconomic, socio-demographic and infrastructure investment scenarios, and will facilitate inter-organisational decision making processes that (i) will enable stakeholders to continually optimise the delivery of services and their infrastructure development plans and (ii) will unlock the development potential of land constained by inadequate provision of infrastructure.

SYNC is web service that enables users to model the interdependencies between multiple urban systems online. The service provides tools to manipulate the physical form, transport infrastructure, land use activity, social, economic and environmental conditions of virtual cities and explore the implications of these changes directly via a web interface. SYNC provides outputs relevant to a wide range of urban planning, design and management problems such as forecasting demographic change, travel patterns, environmental performance, energy consumption, pollution rates and exposures, infrastructure demands, land use and rent distributions, access to health and education resources and socio-economic segregation, all building on relationships between urban sub-systems. SYNC has also been designed to speed up, simplify and automate model procedures and provide a user experience that makes direct online modelling possible. We believe that a successful future for our cities will only be realised if integrated modelling resources are opened up to a much wider market and used more frequently in the planning and design process. We aim to achieve this by providing the technology to democratise the generation of model outputs and help different professional and community stakeholders to collaborate on shared challenges. SYNC is being developed by a team of researchers from University College London and is being supported by UCL Business and The Future Cities Catapult in taking the product to market. Our project will take SYNC from a pre-beta prototype to market release through a structured programme of product development, market research and collaborative engagement with future license holders, developer API users, data providers, web service providers and trials of the consumer service via beta release. We will demonstrate the power of this approach by applying the service to a number of case study projects and undertaking trials of a beta release with members of the prospective user base.