A WiFi-based occupancy sensing, modelling, and simulation method to ensure COVID-19 ventilation and social distancing norms at workplaces

基于 WiFi 的占用感测、建模和模拟方法，可确保工作场所的 COVID-19 通风和社交距离规范

• 批准号: 554565-2020
• 负责人: Gunay, Burak
• 金额: $ 3.64万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705680
• 关键词: WiFi; based; occupancy; sensing; modelling

As provinces are planning for return-to-work, a new challenge that we will face is to practice safe social distancing indoors. Policies to define acceptable occupant densities and interior design configurations are needed. Unlike the safe distancing policies for outdoors, these policies should also consider per-person ventilation requirements to minimize the risk of exposure to infectious aerosols. These policies need to be developed upon high resolution measured occupancy data from shared office spaces. To this end, occupancy sensing solutions offer an untapped opportunity to develop policy recommendations and to regulate these policies. In this project, a team of researchers will partner with two firms specialized in occupancy sensing solutions and study high-resolution occupancy data from real office buildings. The team will develop an agent-based occupancy modelling approach to simulate indoor occupancy patterns for a variety of office layouts. The results of this analysis will lead to policy recommendations for occupant densities and an online interactive tool to simulate occupancy patterns for different occupant densities and office layouts. The team will also conduct a case study demonstrating the use of WiFi-based occupancy sensing technology to regulate occupant density policies in an administrative office building at Carleton University. The proposed research will contribute to our efforts to restart the economy while minimizing the risk of new outbreaks starting at workplaces. The methods developed, once adopted by our partners, will contribute to our knowledge-based economy. One postdoctoral researcher and one Ph.D. student will conduct multidisciplinary research and develop widely sought-after skills in data mining, visualization, and occupant modelling and simulation.

随着各省计划重返工作岗位，我们将面临的一个新挑战是在室内实行安全的社交距离。需要制定政策来定义可接受的居住密度和室内设计配置。与户外的安全距离政策不同，这些政策还应考虑每人的通风要求，以尽量减少接触传染性气溶胶的风险。这些政策需要根据共享办公空间的高分辨率测量占用数据来制定。为此，占用感应解决方案为制定政策建议和规范这些政策提供了一个尚未开发的机会。 在该项目中，研究人员团队将与两家专门从事占用传感解决方案的公司合作，研究来自真实办公楼的高分辨率占用数据。该团队将开发一种基于代理的占用建模方法来模拟各种办公室布局的室内占用模式。该分析的结果将产生针对居住密度的政策建议以及用于模拟不同居住密度和办公室布局的居住模式的在线交互工具。该团队还将开展案例研究，展示如何使用基于 WiFi 的占用传感技术来调节卡尔顿大学行政办公楼的占用密度政策。 拟议的研究将有助于我们重启经济的努力，同时最大限度地降低工作场所爆发新疫情的风险。所开发的方法一旦被我们的合作伙伴采用，将为我们的知识经济做出贡献。博士后1名，博士1名。学生将进行多学科研究，并发展数据挖掘、可视化以及乘员建模和仿真方面广受欢迎的技能。