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

增加铁路运输吞吐量，同时避免损害社交距离的激励措施：基于代理的量化得出指导方针

• 批准号: ES/W000601/1
• 负责人: David Fletcher
• 金额: $ 19.76万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FW000601%2F1
• 关键词: Increasing; rail; transport; throughput; while

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.

公共交通对于经济活动、城市运作和上班至关重要，但也存在许多瓶颈（门、排队受限区域、检票口），社交距离很容易受到影响。这些点决定了人员流动速度，在维持社交距离安全的同时，在实现交通功能方面产生了相互冲突的优先事项。拟议的研究将建立在先前基于代理的铁路平台-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）模拟进行参数探索和灵敏度分析。通过与铁路安全与标准委员会（RSSB）的合作，我们目前的RateSetter模型为铁路部门政策和利益相关者提供了信息。需要探索的其他因素包括：(i)激励措施，如火车即将发车，以损害社会距离。（ii）在复杂密闭空间行人流中个人态势感知的局限性。不同的个人自信及其对密闭空间流动动力学的影响。建模将侧重于优化客流，以避免鼓励破坏社交距离，为有效的时间表和新冠安全车站运营提供指导。在半封锁的情况下，这一点预计非常重要，因为大量铁路乘客可能会在晚些时候接种疫苗，但他们会想再次开始旅行。为了将研究结果转化为政策和实践的可操作见解，在一系列条件下对火车上下车客流时间的验证预测将被转移到RSSB，以输入网络级铁路系统建模。这将揭示个别车站的行为改变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。这项工作将增强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，同时降低破坏社会距离的风险：(i)在受到破坏社会距离激励的情况下，代表人类在受限空间内运动的算法。一个有效的模式，以迅速试验和优化新的运输操作方式，以帮助国家恢复。（iii）量化限制乘客和工作人员接近病毒及累积接近病毒（潜在病毒载量）的干预措施成效的指引。（iv）将有效的客流时间预测输入铁路行业网络模型，以揭示车站管理政策的影响。