Increasing rail transport throughput while avoiding incentives to compromise social distancing: agent-based quantification leading to guidelines

Public transport is crucial to economic activity, functioning cities and access to work, but presents many pinch-points (doors, confined areas of queuing, ticket gates) where social distancing is easily compromised. These points determine people flow rates, creating conflicting priorities in enabling functioning transport while maintaining social distancing safety. Research is proposed building on previous agent-based modelling of passengers at the railway platform-train interface conducted using massively parallel Graphics Processing Unit (GPU) simulations for parameter exploration and sensitivity analysis. Our current RateSetter model has informed rail sector policy and stakeholders through collaboration with Railway Safety and Standards Board (RSSB). Additional factors to be explored include: (i) Incentives such as imminent train departure to compromise social distancing. (ii) Limitations on personal situational awareness in complex confined space pedestrian flows. (iii) Differing personal assertiveness and its impact on confined space flow dynamics. Modelling will focus on optimisation of passenger flow to avoid incentivising compromised social distancing, providing guidelines on effective timetabling and COVID safe station operation. RSSB will facilitate data access, knowledge exchange and dissemination within the rail industry. The work will increase confidence in rail use and enable higher passenger volumes with lower risk of compromised social distancing through: (i) Algorithms representing human movement in confined spaces subject to incentives to compromise social distancing. (ii) A validated model to rapidly test and optimise new ways of operating transport to aid national recovery. (iii) Guidelines on quantification of intervention effectiveness in limiting proximity and cumulative proximity (potential viral load) for passengers and staff.