Discovering flow patterns across spatial and temporal scales

我们正在见证一场大规模收集移动性数据的范式转变，这在以前是不可能的。特别是，使用高空间分辨率卫星、交通检测摄像机、卫星全球定位系统(GPS)和移动电话网络等一系列技术收集了车辆机动性数据。尽管技术在不久的将来会带来“随时随地数厘米”的情景，但最近的研究表明，由于使用单一数据源，目前生成的车辆机动性数据存在人口规模有限和实验持续时间短的问题。针对不同来源(如车载GPS和交通摄像头)的车辆流动数据的跨尺度效应问题，提出了一种无尺度的地理知识过程来揭示流动模式的形成、结构和动态。流动模式本质上不是随机的，因为它们在空间(例如，从车辆、交叉路口、高速公路到网络)以及时间(例如，从几分钟到一小时)上具有无标度结构。将开发新的流动性测量和流型技术，以缓解由于使用不同数据源而造成的规模问题。这项研究的成功还将有助于缩小大量收集的车辆机动性数据与目前从这些数据中提取的有限流型信息之间的知识差距。它将帮助用户有效地利用他们的应用程序中发现的流量模式，如智能交通系统、未来的智能城市和大规模紧急疏散规划。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球测量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.