Rebuilding confidence Helping Transport for London understand and manage risk and build pandemic resilience.
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Context In re-opening their networks, transport operators need to rebuild people’s confidence and take reasonable steps to reduce employees’ and passengers’ exposure to the COVID-19 virus. In order to do this, they need to communicate clearly and strike a balance between risks and operations, based on the science, and to be one step ahead of the next turn in events.
There are, however, many challenges in predicting how events will unfold. Local approaches to controlling the disease will likely remain dynamic due to the varying peaks and troughs of subsequent waves, while the timelines for the development, manufacture and distribution of a potential vaccine continue to evolve. There are multiple scenarios that can unfold, and operators need to be prepared for each one. It is possible that attention will at some point be targeted on London Underground if, for example, a specific route becomes a common factor among a group of people who test positive for COVID-19. In scenarios such as this – and particularly because staff and passenger confidence are of utmost importance – clearer understanding and communication of the specifics of possible transmission within London Underground environments are vital.
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Considerations for the London Underground Infrastructure epidemiology can help us identify and manage transmission risks across your network.
To overcome these, there is a need to understand both the epidemiology of the virus and components that make the infrastructure functional, such as airflow dynamics, and pedestrian and human behaviour.
Users of the London Underground – both staff and passengers – engage the system through a series of stations, tunnels and train carriages. As with interactions in most public settings, there is a risk of transmitting the COVID-19 virus. There are, however, additional complications at the user interface, such as ticketing bottlenecks, crowding on enclosed platforms, and constraints of airflow and ventilation.
Key questions have arisen about our ability to assess risk and to communicate the findings, and we have combined our expertise to consider ways in which this might be addressed. By applying our infrastructure epidemiology lens, we can combine our specific expertise into London Underground and model this with our epidemiologists’ understanding of COVID-19 transmission and behaviour change. This enables us to develop an understanding of the risks and the business implications – both for short-term and for longer-term resilience – that are specific to London Underground.
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What can we do to assess risk?
Airflow in stations The virus that causes COVID-19 is mainly transmitted through respiratory droplets that are exhaled, coughed or sneezed from infectious individuals. Recent evidence suggests that some of the smaller viral particles can remain in the air for longer, perhaps even hours. It is therefore increasingly important to have a good grasp of how air behaves inside belowground systems and how this can be modified.
Computational fluid dynamics (CFD) analysis can provide a more detailed description of airflow patterns and temperature distributions in localised areas such as entrances and junctions.
Air movement in tunnels is primarily regulated by the train piston effect, pushing air through the system. On many lines, this is enhanced by large extraction fans that draw in air from outside. We can use a model to test several variables to see how they may affect the air inside tunnels. Things to consider include turbulence, airflow, ventilation systems, pedestrian flows and temperature modification – all of which may change how the air behaves and how people might be exposed to it. Numerical modelling can be used to assess the impact of different control or mitigation options on air flowrates, airspeeds, air temperature and pollutant concentrations throughout the underground network. One dimensional (1D) ventilation analysis can provide a description of the variations of bulk airspeeds throughout the tunnels, platforms and passageways.
Airflow in carriages Airflow in carriages also has its own dynamics. This can be affected by use of onboard fans or opening windows. Some current vehicles also drag air towards a central extraction point, potentially increasing risk at the centre of the carriage. Other considerations include the introduction of filters or ultraviolet-C lamps for onboard heating, ventilation and air conditioning (HVAC) climate control systems. Examples are in development or are still being tested, and proof in service and for extended life would be required, but we may be able to use our models to assess their relative effectiveness.
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Pedestrian flows in stations We can model a station to analyse several scenarios. Modelling can help provide insight into how queuing at ticket booths, gates and crowding on platforms may be affected when social distancing is implemented. It can also identify key risk areas for transmission of the COVID-19 virus by identifying congested areas and analysing how long people are in close proximity to each other. Furthermore, modelling can identify operational challenges in stations due to social distancing, for example, reduced capacity of stations and trains, or a longer dwell time period for trains. Operational measures, such as one-way systems, can be tested to
assess whether they are effective in reducing proximity between passengers and to identify the impact on passenger journey times and station capacity. Each station along a network can be modelled independently, which will be important to reflect local or regional lockdowns and variations in guidelines and policy. A model can also be adjusted for peak travel periods, reflecting anticipated changes in demand. Passenger modelling at potential bottlenecks, such as ticket booths, corridors or stairs, can be used to find an acceptable balance in capacity and to manage transmission risk.
Images 1 and 2 below show a pedestrian model where social distancing was applied. If circles overlap this indicates that people did not adhere to the social distance specified. Image 1
Image 2
The cumulative mean density maps, as shown in images 3 and 4, indicate how many people occupy a station platform. The maps show a comparison between normal operations (image 3) and where social distancing is applied. (image 4).
Images 5 and 6 show the difference between a normal operations model where no social distancing is implemented (image 5) and a model where social distancing is implemented (image 6). This type of modelling can be used to assess the impact on the capacity of different infrastructural elements within a station, such as stairs, escalators and ticket gates. In this example staircase capacity was reduced by 85%.
Image 3
Image 5
Image 4
Image 6
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Touchpoints and behaviour change Finally, both staff and passengers can reduce their risk of transmitting or catching the virus that causes COVID-19 by reducing the number of objects or surfaces they touch.
Analysis of touchpoints and the use of heatmaps where people generally touch or share surfaces can help to highlight areas of high risk. These, in turn, can be reviewed and assessed in order to reduce risk by applying various mitigation measures, such as employing digital tools and contactless technologies.
Business as usual
Reduced risk
Key
COVID passenger entering station
Passenger entering station
Probable contamination
COVID passenger exiting station
Passenger exiting station
Possible contamination
We can attempt to quantify changes in risk that result from these mitigation measures. As with all models, we can further adjust scenarios to reflect possible changes in local transmission and changes to local regulations that may come into force.
As we have already seen, many functions can be replaced by digital and contactless technologies. However, by better understanding the risks we can do more to improve compliance and assure staff and customers that the right approach has been taken.
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What can we do with this information?
Through our understanding of epidemiology, we have built on our existing models to make them more relevant to COVID-19, helping us to focus on, understand and manage key risks. We will continue to do this as our understanding of the science and risk evolves.
Using this science-led approach and communicating it clearly to staff and passengers should help improve people’s confidence that it is safe to use the London Underground network. Combining our understanding of how the COVID-19 virus might carry in air with our experience of analysing air movement and particles in below-ground stations and tunnels can help us assess risk in different scenarios. This includes whether it is better to increase or decrease air movement for people walking through the system or where they are stationary, such as when waiting for trains to arrive or in control rooms, staff meeting rooms or mess facilities. By understanding how air flows through the carriage it may be possible to determine areas of higher or lower risk. It may also be possible to adjust the temperature of a platform and, undertaking some behavioural operational research, to assess whether it improves compliance with using face coverings. When we combine these models with our pedestrian analysis, we can begin to build a picture of where the risks might be greatest – but also to assess how interventions may affect residual risk and its impact on operations.
For example, if ticket gates were opened, if one-way systems were introduced, or if stations were restricted, we can assess the impact these measures may have on social distancing, transmission risk, and other operational considerations, such as busy stations or at busy times. A more analytical approach to assessing risk can provide confidence – particularly for staff and their unions – that mitigation strategies can reduce risk in a quantifiable way. For decision-makers, these tools can be used to assess how new ways of working or new technologies should be prioritised against other mitigation strategies. Importantly for all, a more comprehensive understanding of risk can also be used to demystify perceived risks of using public transport. A well-explained evidence base can lead to well-designed policy, which in turn can improve compliance and reassure staff and passengers alike on the measures being promoted. Working with you, we can help to find an acceptable balance between capacity and risk, and to test these approaches across a number of possible scenarios. As the pandemic evolves, and we continue to follow its epidemiology, we are well-placed to ensure the new science feeds into how we understand risk.
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Opening opportunities with connected thinking. For more information, contact: Neil Henderson Key account leader for Transport for London neil.henderson@mottmac.com
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