III: Small: Real-World Traffic Data Management for Time-Dependent Spatial Queries

III：小型：用于时间相关空间查询的真实交通数据管理

• 批准号: 1115153
• 负责人: Cyrus Shahabi
• 金额: $ 49.96万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115153&HistoricalAwards=false
• 关键词: III; Small; Real; World; Traffic

The two most important commodities of the 21st century are time and energy; traffic congestion wastes both. Several disciplines have studied the traffic congestion problem using mathematical models, simulation studies and field surveys. Recently, due to the sensor instrumentation of road networks and the availability of commodity sensors from which traffic information can be derived (e.g., CCTV cameras, GPS devices), a large volume of real-time traffic data at high spatiotemporal resolutions has become available. These data lay the ground for better understanding of the transportation network and for new applications.The first objective of this project is to study the efficient acquisition and storage of traffic data. In particular, at the acquisition time, compact traffic patterns and outliers are constructed from raw data in real-time, supporting fast queries with approximate results and error bounds. The second objective is to utilize the traffic patterns to enable two critical query types that are building blocks of several important applications in time-dependent road networks: 1) shortest travel-time computation, and 2) k-nearest-neighbor search.The broader impact of this project is to bring large amount of historical and real-time traffic data at the fidelities unseen before to the fingertip of transportation engineers, policy makers and end-users. This in turn enables them to alleviate one of the major problems of megacities, the main societal fabrics of the 21st century. Research results are incoporated into courses on Database Systems and Geospatial Information Management taught at University of Southern California and are also disseminated through papers published in conferences and journals. Details about the project including publications and open-source code are available at the project website: http://infolab.usc.edu/projects/TransDec/.

21世纪最重要的两种商品是时间和精力；交通拥堵浪费了这两样东西。一些学科使用数学模型、模拟研究和实地调查来研究交通拥堵问题。近年来，由于道路网络的传感器仪表以及可从中获取交通信息的商品传感器(如CCTV摄像机、GPS设备)的出现，大量高时空分辨率的实时交通数据成为可能。这些数据为更好地理解交通网络和新的应用奠定了基础。本项目的第一个目标是研究交通数据的有效获取和存储。特别是，在采集时，从原始数据实时构建紧凑的流量模式和离群值，支持具有近似结果和误差界的快速查询。第二个目标是利用交通模式来支持两种关键的查询类型，它们是时间相关道路网络中几个重要应用的构建块：1)最短旅行时间计算，2)k最近邻搜索。该项目的更广泛的影响是将大量的历史和实时交通数据以前所未有的保真度带到交通工程师、政策制定者和最终用户的指尖。这反过来又使它们能够缓解21世纪主要社会结构--特大城市的主要问题之一。研究成果被纳入南加州大学讲授的数据库系统和地理空间信息管理课程，并通过在会议和期刊上发表的论文进行传播。有关该项目的详细信息，包括出版物和开放源代码，请访问该项目的网站：http://infolab.usc.edu/projects/TransDec/.