66086FlowOS Bus Occupancy EstimationClosedFeasibility StudiesInnovate UKThe 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.