Agent-Based Modelling and Dynamic Data Assimilation for Modelling Urban Dynamics

用于城市动力学建模的基于代理的建模和动态数据同化

• 批准号: 1945083
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1945083
• 关键词: Agent; Based; Modelling; Dynamic; Data

Identifying the factors that encourage and discourage attendance in urban spaces is vital from both academic and practitioner perspectives. For policy makers, an understanding of what attracts people to city centres, and what discourages them, is vital for urban planning and emergency management. From an academic perspective, understanding the drivers of footfall is essential in order to answer questions of mobility, inclusivity, and accessibility of opportunities.Simply quantifying footfall, let alone dissecting the underlying drivers, is extremely challenging. Although there are a series of diverse 'big' datasets that are emerging which can contribute to footfall estimates, none in isolation provide a comprehensive picture. In addition, there are no standard methods that are appropriate for assimilating dynamic, diverse, noisy, and biased data sources to create a complete picture.To address these gaps in our understanding of urban dynamics, this project will embark on an ambitious programme of methodological development and empirical data analysis. It will adapt relevant methods from fields such as computer science (e.g. machine learning and artificial intelligence), atmospheric modelling (e.g. data assimilation and ensemble modelling), and geography (e.g. GIS and spatial analysis) to create a robust model of footfall. Indicative data source that will underpin the analysis include: footfall data collected by Leeds City Council CCTV cameras; Census workday population estimates; dynamic weather data; geo-located Twitter data; times of public events and holidays; business opening hours; Wi-Fi sensor footfall data; and others as they become available. There is great potential to leverage these data as both an explanatory tool for understanding what has been driving city-centre attendance and as a predictive tool for forecasting future footfall under different scenarios. Leeds City Council are actively involved in designing the research questions, identifying data sources, and will jointly supervise the research. This project will feed into work across the CDRC remit, providing baseline and temporally nuanced urban populations, along with an explanation of their drivers. It will feed into real-world urban management systems at Leeds City Council, as well as broader cities and future cities literature. Finally, it will act as a foundation for future work in emergency planning and diurnal population movements, as well as longer-term predictions of city accessibility.

从学术和实践的角度来看，确定鼓励和阻止城市空间出席的因素至关重要。对于决策者来说，了解什么吸引人们到城市中心，什么阻碍他们，对于城市规划和应急管理至关重要。从学术角度来看，了解客流量的驱动因素对于回答流动性、包容性和机会可及性问题至关重要。简单地量化客流量，更不用说剖析潜在的驱动因素了，这是极具挑战性的。虽然有一系列不同的“大”数据集正在出现，可以有助于客流量估计，没有一个孤立地提供一个全面的图片。此外，目前还没有一个标准的方法来吸收动态的、多样化的、嘈杂的和有偏见的数据源，以创造一个完整的画面。为了解决我们对城市动态的理解中的这些差距，本项目将着手一个雄心勃勃的方法发展和经验数据分析计划。它将采用计算机科学（如机器学习和人工智能）、大气建模（如数据同化和整体建模）和地理学（如地理信息系统和空间分析）等领域的相关方法，以创建一个稳健的足迹模型。支持分析的指示性数据源包括：利兹市理事会闭路电视摄像机收集的客流量数据;人口普查工作日人口估计;动态天气数据;地理定位的Twitter数据;公共活动和假日时间;营业时间; Wi-Fi传感器客流量数据;以及其他可用数据。利用这些数据作为解释性工具来了解是什么推动了市中心的出勤率，并作为预测不同情景下未来客流量的预测工具，潜力巨大。利兹市理事会积极参与设计研究问题，确定数据来源，并将共同监督研究。该项目将为整个CDRC的工作提供信息，提供基线和时间上细微差别的城市人口，沿着对其驱动因素的解释。它将为利兹市理事会的现实城市管理系统以及更广泛的城市和未来城市文献提供信息。最后，它将作为今后应急规划和每日人口流动以及城市可达性长期预测工作的基础。