AF: Small: Geometric Algorithms for Constructing Road Networks from Trajectories

AF：小：根据轨迹构建道路网络的几何算法

• 批准号: 1216602
• 负责人: Carola Wenk
• 金额: $ 30.36万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216602&HistoricalAwards=false
• 关键词: AF; Small; Geometric; Algorithms; Constructing

The objective of this project is to develop geometric algorithms with quality and performance guarantees for constructing road networks from geo-referenced trajectory data. This is a new type of geometric reconstruction problem in which the task is to extract the underlying geometric structure sampled by a a set of noisy, movement-constrained trajectories. Different models for trajectories and for road networks will be investigated. Ideas from geometric shape matching, probabilistic modeling, and trajectory clustering will be applied to develop geometric algorithms with quality and performance guarantees that exploit the continuity of the input trajectories. Proof-of-concept implementations will be developed to test and validate the algorithms on real data.Vast amounts of geo-referenced trajectory data are being collected due to the ubiquitous availability of positioning technologies such as the Global Positioning System (GPS). This project will help address the very timely challenge of analyzing this data. It will also provide novel algorithms for construction and maintenance of digital street maps, which are among the most valuable digital data resource in today's society. The results of this project will benefit a wealth of applications ranging from a variety of location-based services on street maps to the analysis of tracking data for hiking trail map generation or for studying social behavior in animals. Students will be tightly integrated into research projects, providing them with collaborative research experience.

该项目的目的是开发具有质量和性能保证的几何算法，可从地理参考轨迹数据中构建道路网络。这是一种新型的几何重建问题，其中任务是提取一组嘈杂的，运动约束的轨迹采样的基本几何结构。将研究轨迹和道路网络的不同模型。几何形状匹配，概率建模和轨迹聚类的想法将用于开发具有质量和性能的几何算法，可确保利用输入轨迹的连续性。将开发概念验证实现，以测试和验证实际数据上的算法。由于位置技术的无处不在（例如全球定位系统（GPS））的无处不在，正在收集地理参考轨迹数据的vast量。 该项目将有助于解决分析此数据的及时挑战。它还将为数字街道地图的构建和维护提供新颖的算法，这些算法是当今社会中最有价值的数字数据资源之一。 该项目的结果将使大量应用程序受益，从街道地图上的各种基于位置的服务到远足步道图生成的跟踪数据的分析或研究动物中的社交行为。学生将紧密整合到研究项目中，为他们提供协作研究经验。