Hybrid physical-statistical models for air quality prediction from traffic data

根据交通数据预测空气质量的混合物理统计模型

• 批准号: 2107396
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2107396
• 关键词: Hybrid; physical; statistical; models; air

In the present study, we shall develop a hybrid physical-statistical model for air quality prediction. The research is a collaborative work between AVL and the EPSRC Centre for Doctoral Training Statistical Applied Mathematics (SAMBa). The project aims to combine deterministic and statistical modelling to forecast air pollution levels within a city. The focus is on air quality change resulting from traffic patterns, vehicle types, urban layout and meteorological conditions. The aim is to move towards incorporating real time data from traffic and pollution monitors to inform the forecast. It is also an aim to develop methods that can distinguish the relative impact of traffic from other sources of pollution. First, a physical and chemical model will be used to describe automobile traffic and pollutant concentrations. A statistical model will be used to analyse data provided by traffic and pollution monitoring sensors located in a city. Subsequently, the statistical model will be used to identify and account for deviations from the assumptions of the physical model as well as to calibrate the model parameters. Finally, the goal will be to develop a hybrid system where both physical and statistical models work together to improve forecasts. The supervisory team consists of Prof. Paul Milewski who is an expert on physical modelling of fluid and continuum processes, and Dr Theresa Smith who is an expert on spatial statistics. Together they span the expertise needed for the project. The AVL contact is Gerhard Schagerl who will provide guidance on the industrial impact of the project.

在这项研究中，我们将开发一个用于空气质量预测的混合物理统计模型。这项研究是AVL和EPSRC统计应用数学博士生培训中心(Samba)的合作成果。该项目旨在将确定性建模和统计建模相结合，以预测城市内的空气污染水平。重点是交通模式、车辆类型、城市布局和气象条件导致的空气质量变化。其目标是将来自交通和污染监测的实时数据纳入预测。它的目标也是开发能够区分交通与其他污染源的相对影响的方法。首先，将使用一个物理和化学模型来描述汽车交通和污染物浓度。统计模型将用于分析位于城市的交通和污染监测传感器提供的数据。随后，将使用统计模型来确定和说明与物理模型假设的偏差，以及校准模型参数。最后，我们的目标将是开发一种混合系统，在该系统中，物理模型和统计模型共同工作，以改进预测。监督小组由流体和连续介质过程的物理模型专家Paul Milewski教授和空间统计专家Theresa Smith博士组成。他们一起跨越了项目所需的专业知识。AVL的联系人是Gerhard Schagerl，他将就该项目的行业影响提供指导。