City Modelling Lab - Alpha Pandemic Activity Modelling to help our cities reopen safely

城市建模实验室 - Alpha 流行病活动建模可帮助我们的城市安全重新开放

• 批准号: 62766
• 金额: $ 6.28万
• 依托单位: OVE ARUP & PARTNERS LIMITED
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=62766
• 关键词: City; Modelling; Lab; Alpha; Pandemic

As the coronavirus descended on the UK, the initial focus of our city councils and transport agencies has been on coping with managing unprecedented changes to daily life and preparing to support healthcare workers. The next big focus for these decision makers will be planning services to support a managed transition out of the current lock-down. To that end, the modelling work which Arup has developed could help shed light on critical trade-offs between restarting economic activity and managing health risks.We have developed agent based models (ABMs) to simulate how cities operate - using granular data that reflect collective decisions of individuals (description of agent based models here: [https://medium.com/arupcitymodelling/def-city-modelling-5f8be67c1c2][0]). With this modelling approach, we can help cities to plan for their economic recovery over the next weeks and months. Which transport services are most important to connect key-workers to their work? How will public transport operators maximise access to key work sites and potentially new schedules (e.g. staggered school days) while mitigating crowding? We have explored how ABMs can identify social networks: [https://medium.com/arupcitymodelling/lab-note-003-agent-to-agent-interactions-e013d594db7f][1]. Over the coming months, what might longer-term changes in transport demand mean for fare-based revenue? Traditional transport models are not designed to solve these challenges. They make use of aggregate data, which limits them in simulating these new scenarios and they are slow to build.We have built agent based models with Transport for London, Melbourne, Transportation Infrastructure Ireland, and the Ministry of Transport in New Zealand (read about case studies here: [https://medium.com/arupcitymodelling/agent-based-models-in-action-f05010567c54][2]). To deliver these models, we built from robust tooling developed in academia. We are confident in the design, feasibility, and application of these models. What we have not tested is how we might incorporate radically different behaviours, for which there is no precedent.This project would test these models in an unprecedented context.We propose building an alpha model for one city, over 6 weeks. We will be able to assess the technical viability of agent based models as tools to support decision-makers in this unique time. We will also be able to explore how this modelling approach may fit within the organisational and governance structures of cities/transport authorities. We have been in conversation with Transport for London and Glasgow City Council - as potential partners for this testing - should this application be successful.[0]: https://medium.com/arupcitymodelling/def-city-modelling-5f8be67c1c2[1]: https://medium.com/arupcitymodelling/lab-note-003-agent-to-agent-interactions-e013d594db7f[2]: https://medium.com/arupcitymodelling/agent-based-models-in-action-f05010567c54

随着冠状病毒袭击英国，我们的市议会和交通机构最初的重点是应对日常生活中前所未有的变化，并准备支持医护人员。这些决策者的下一个重点将是规划服务，以支持摆脱当前封锁的可控过渡。为此，奥雅纳开发的建模工作可以帮助阐明重新启动经济活动和管理健康风险之间的关键权衡。我们开发了基于代理的模型（ABM）来模拟城市的运作方式 - 使用反映个人集体决策的精细数据（此处基于代理的模型的描述： [https://medium.com/arupcitymodelling/def-city-modelling-5f8be67c1c2][0]）。通过这种建模方法，我们可以帮助城市规划未来几周和几个月的经济复苏。哪些交通服务对于连接关键员工和他们的工作最重要？公共交通运营商将如何最大限度地利用关键工作地点和潜在的新时间表（例如错开上学日），同时缓解拥挤？我们探索了 ABM 如何识别社交网络：[https://medium.com/arupcitymodelling/lab-note-003-agent-to-agent-interactions-e013d594db7f][1]。未来几个月，交通需求的长期变化对票价收入意味着什么？传统的运输模式并不是为了解决这些挑战而设计的。他们利用汇总数据，这限制了他们模拟这些新场景，而且构建速度很慢。我们与伦敦交通局、墨尔本交通局、爱尔兰交通基础设施局和新西兰交通部建立了基于代理的模型（在此处阅读案例研究：[https://medium.com/arupcitymodelling/agent-based-models-in-action-f05010567c54][2]）。为了提供这些模型，我们使用学术界开发的强大工具进行构建。我们对这些模型的设计、可行性和应用充满信心。我们还没有测试的是如何整合完全不同的行为，这是没有先例的。这个项目将在前所未有的背景下测试这些模型。我们建议在 6 周内为一个城市建立一个 alpha 模型。我们将能够评估基于代理的模型作为在这个独特时期支持决策者的工具的技术可行性。我们还将能够探索这种建模方法如何适应城市/交通当局的组织和治理结构。我们一直在与伦敦交通局和格拉斯哥市议会（作为本次测试的潜在合作伙伴）进行对话，如果此应用程序成功的话。[0]：https://medium.com/arupcitymodelling/def-city-modelling-5f8be67c1c2[1]： https://medium.com/arupcitymodelling/lab-note-003-agent-to-agent-interactions-e013d594db7f[2]：https://medium.com/arupcitymodelling/agent-based-models-in-action-f05010567c54