Discovering flow patterns across spatial and temporal scales

发现跨空间和时间尺度的流动模式

• 批准号: 418205-2013
• 负责人: Wachowicz, Monica
• 金额: $ 1.75万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573537
• 关键词: Discovering; flow; patterns; across; spatial

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.

我们正在见证一种范式转变，即以以前不可能的规模收集流动性数据。特别是，车辆移动数据是利用一系列技术收集的，如高空间分辨率卫星、交通检测摄像机、卫星全球定位系统和移动的电话网络。虽然技术在不久的将来会推出“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，今天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。流型发现带来了额外的挑战，如何处理跨尺度的影响时，使用车辆的移动数据集从不同的来源（如车载GPS和交通摄像头）。 本研究旨在发展一个无标度的地理知识过程，以揭示流态的形成、结构和动力学。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，从几分钟到一小时的时间）中具有无标度结构。将开发新的流动性测量和流动模式技术，以减轻由于使用不同数据源而产生的规模问题。这项研究的成功结果也将有助于缩小大量收集的车辆移动数据和从这些数据中提取的流量模式信息的能力非常有限之间的知识差距。它将帮助用户在其应用程序中有效地利用所发现的流模式，例如智能交通系统，未来的智能城市和大规模紧急疏散规划。高素质的研究人员和工程师在拟议的研究领域的教育是保持加拿大在地理信息学的领先地位的战略重要性。