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

公共交通对经济活动、城市运转和上班通道至关重要，但也存在许多瓶颈(门、排队的狭窄区域、检票口)，在这些地方，社会距离很容易受到影响。这些点决定了人流速度，在确保正常运行的交通工具和保持社会距离安全的同时，产生了相互冲突的优先事项。拟议的研究将建立在以前基于代理的铁路站台-列车界面乘客建模的基础上，使用大规模并行图形处理单元(GPU)模拟来进行参数探索和敏感性分析。我们目前的RateSetter模式通过与铁路安全和标准委员会(RSSB)的合作，为铁路部门政策和利益相关者提供了信息。其他有待探讨的因素包括：(I)诱因，例如即将开行的列车，以调和社会距离。(2)在复杂的受限空间人流中对个人情景感知的限制。(3)不同的个人自信及其对受限空间流动动力学的影响。模型将侧重于优化客流，以避免激励妥协的社会距离，提供有效的时刻表和COVID安全车站运营的指导方针。预计这在半封锁的情况下将非常重要，因为大量铁路乘客可能会在随后的队列中接受任何疫苗接种，但会希望再次开始旅行。为了将调查结果转化为政策和实践中可操作的见解，对一系列条件下列车上落客流时间的有效预测将传输到RSSB，以输入到网络级轨道系统建模。这将揭示个别车站的行为变化和客流管理对整个网络的影响。RSSB将促进铁路行业内的数据获取、知识交流和传播。这项工作将增加人们对铁路使用的信心，并通过以下方式实现更高的客运量和更低的社会距离风险：(I)表示受限空间中人的运动的算法，受影响社会距离的激励。(2)一个经过验证的模式，以快速测试和优化新的运输运营方式，以帮助国家复苏。(3)关于量化限制乘客和工作人员接近程度和累积接近程度(潜在病毒载量)的干预效果的准则。(Iv)将有效的客流时间预测输入到整个铁路行业网络建模，以揭示车站管理政策的影响。

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.The proposed research will build 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. This is expected to be very important in a semi-lockdown situation as large numbers of rail passengers are likely to be in the later cohorts to receive any vaccination yet will want to begin travelling again. To convert the findings to actionable insights for policy and practice validated predictions of passenger flow times for train boarding and alighting under a range of conditions will be transferred to RSSB for input to network level rail system modelling. This will reveal the network wide implications of behavioural change and management of passenger flow at individual stations. 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. (iv) Input of validated passenger flow time predictions to rail industry network wide modelling to reveal impacts of station management policies.