Discovering flow patterns across spatial and temporal scales

我们正在见证一种模式的转变，即以以前不可能的规模收集移动数据。特别是，车辆移动数据的收集使用了一系列技术，如高空间分辨率卫星、交通探测摄像机、卫星支持的全球定位系统（GPS）和移动电话网络。虽然技术在不久的将来揭示了“随时随地厘米”的场景，但最近的研究表明，由于使用单一数据源，目前生成的车辆移动数据受到人口规模有限和实验持续时间短的影响。当使用来自不同来源的车辆移动数据集（例如车载GPS和交通摄像头）时，流量模式发现带来了如何处理跨尺度效应的额外挑战。本研究旨在建立一个无标度的地理知识过程来揭示流型的形成、结构和动态。流量模式本质上不是随机的，因为它们在空间（例如从车辆、十字路口、高速公路到网络）和时间（例如从几分钟到一小时的时间）上都具有无尺度结构。将开发新的流动性测量和流型技术，以减轻由于使用不同数据源而造成的规模问题。这项研究的成功结果也将有助于缩小目前收集的大量车辆移动数据与从这些数据中提取的非常有限的流模式信息之间的知识差距。它将帮助用户在智能交通系统、未来智能城市和大规模紧急疏散计划等应用中有效地利用发现的流量模式。在拟议的研究领域培养高素质的研究人员和工程师对于保持加拿大在地理信息学方面的领先地位具有重要的战略意义。

We are witnessing a paradigm shift in gathering mobility data at scales that were not possible before. In particular, vehicle mobility data has been collected using a range of technologies such as high spatial resolution satellites, traffic detection video cameras, satellite-enabled Global Positioning System (GPS) and mobile phone networks. Although technology unveils a "centimetres everywhere anytime" scenario in the near future, recent research has demonstrated that vehicle mobility data being generated today suffers from limited population sizes and short experimental duration due to the use of a single data source. Flow pattern discovery brings additional challenges on how to handle cross-scale effects when using vehicles mobility data sets from different sources (e.g. vehicle mounted GPS and traffic cameras).This research aims to develop a scale-free geographic knowledge process for uncovering the formation, the structure and the dynamics of flow patterns. Flow patterns are inherently not random as they have a scale-free structure in space (e.g. from vehicles, crossings, highways, to networks) as well as time (e.g. from minutes to an hour time). New mobility measurement and flow pattern techniques will be developed to mitigate the scale issues due to the use of different data sources. The successful outcome of this research will also contribute to narrow the knowledge gap between the vastly collected vehicle mobility data and very limited capacity of flow pattern information extracted from this data today. It will help users to effectively utilize the discovered flow patterns within their applications such as Intelligent Transportation Systems, Smart Cities of the future, and large-scale Emergency Evacuation Planning. The education of high quality researchers and engineers in the proposed research field is strategically important for keeping Canada's leading position in geomatics.