It’s been observed that social distancing has usually been maintained on platforms prior to train arrival, but rarely maintained at train doors during the boarding and alighting process. The primary driver of change from pre-pandemic behaviour has been the level of social distancing, and the number of people boarding and alighting. Differences in rolling stock type (intercity vs suburban) and platform widths had only secondary influence on the passenger flow time results.

Movement along corridors and bridges was predicted to become congested at lower head-counts; this is of particular interest to station staff, who should be alert to the possibility of congestions even when stations are less busy.
In most circumstances, observations show people are motivated to keep moving more than they are to rigidly maintain distance (as with rigid adherence to distancing there would be slow movement in a wide range of situations, even for 1m social distancing). This “compromised social distancing” behaviour was observed particularly near the train doors. The work didn’t explore why passengers compromised their distancing when close to the train, whether this could be down to current operations and dwell times promoting this behaviour, or other factors.

Linear spaces uni-directional and bi-directional movement was simulated for corridors, stairs and open ticket gates. All these simulations pointed to a reduction of aggregate movement speed with social distancing, especially as people density (people per square metre) rises. The combined effect of rising people density and simultaneous reduction of movement speed gives a flow which initially rises but then falls towards zero as a congestion situation develops.

We are currently looking into industry-led case studies to further validate the findings. Interested operators can contact RSSB.

The University of Sheffield team has received further funding by UKRI until November 2021 (ESRC Project Reference ES/W000601/1) to carry out more detailed people movement modelling. This work is looking at behaviour of passengers travelling in groups and those travelling with luggage. The work will also increase confidence in the modelling and input conditions studied by analysing CCTV from more locations and observing changes in public behaviour as the pandemic evolves. This is expected to include issues such as a change in the mix of travellers to include those who have been vaccinated alongside those who have not.

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Ratesetter+: Report 1 - Platform and Station Passenger Flow Optimisation (COF-G22-01 Report)

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