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

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一直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主要关注点将是规划服务，以支持从当前的封锁中有管理地过渡。为此，奥雅纳开发的建模工作可能有助于阐明重启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)来模拟城市如何运行-使用反映个人集体决策的粒度数据(此处描述基于代理的模型：[https://medium.com/arupcitymodelling/def-city-modelling-5f8be67c1c2][0]).通过这种建模方法，我们可以帮助城市为未来几周和几个月的经济复苏做好规划。将关键员工与工作联系起来，最重要的是哪些交通服务？公共交通运营商将如何在缓解拥挤的同时，最大限度地利用关键工作地点和潜在的新时间表(例如错开的上学日)？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].在未来数月，运输需求的较长期变化对以票价为基础的收入会有何影响？传统的运输模式并不是为解决这些挑战而设计的。他们使用聚合数据，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一起构建了基于代理的模型(点击此处阅读案例研究：[https://medium.com/arupcitymodelling/agent-based-models-in-action-f05010567c54][2]).为了交付这些模型，我们利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，我们可能会如何整合截然不同的行为，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阿尔法模型。我们将能够评估基于代理的模型作为工具在这个独特的时代支持决策者的技术可行性。我们还将能够探索这种建模方法如何适用于城市/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进行谈判--作为这项测试的潜在合作伙伴--如果这项申请成功的话。[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

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