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能量。交通拥堵两者都浪费了。几个学科使用数学模型，模拟研究和现场调查研究了交通拥堵问题。 最近，由于道路网络的传感器仪器以及商品传感器的可用性，可以从中得出交通信息（例如CCTV摄像机，GPS设备），因此可以提供大量的高时空分辨率实时交通数据。 这些数据为更好地理解运输网络和新应用程序奠定了基础。该项目的第一个目标是研究流量数据的有效获取和存储。 特别是，在获取时间，紧凑的流量模式和离群值是实时由原始数据构建的，支持具有近似结果和错误范围的快速查询。 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. 反过来，这使他们能够减轻21世纪主要社会面料的主要问题之一。研究结果未涉及在南加州大学教授的数据库系统和地理空间信息管理的课程中，并且还通过会议和期刊上发表的论文进行了传播。有关该项目的详细信息，包括出版物和开源代码，可在项目网站上找到：http：//infolab.usc.edu/projects/transdec/。