Exploiting Live Plus Archive Data for Intelligent Transportation Systems

利用 Live Plus 存档数据实现智能交通系统

• 批准号: 0612311
• 负责人: David Maier
• 金额: $ 41.07万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0612311&HistoricalAwards=false
• 关键词: Exploiting; Live; Plus; Archive; Data

Traffic congestion and the associated delay and economic costs it causes are a source of significant concern. In the United States over the past twenty years, vehicle miles traveled for passenger cars grew 44%, but miles of interstate highway increased less than 8%! In response, transportation departments are moving towards intelligent transportation management through the use of tools such as adaptive ramp meters and traffic signals, and expanded traffic information systems. Much of the data available for use in intelligent transportation management is in the form of data streams, such as inductive loop detector data, Automatic Vehicle Location (AVL) systems on buses, and live traffic signal data.This project investigates the use of Data Stream Management Systems (DSMS) for Intelligent Transportation Systems (ITS). Current DSMS technology is not adequate for ITS applications; ITS data is disordered, dirty and bursty and can arrive from widely varied sources (embedded detectors, active vehicles, passive transponders). Further, the live data must be compared with archived historical data and other types of information sources. In addition to the current focus on temporal aggregation, ITS data requires spatial and potentially spatio-temporal aggregation. Finally, any DSMS that processes ITS data must scale to thousands or tens of thousands of simultaneous queries.The goals of this research are to extend the NiagaraST stream-processing system to accommodate queries that arise in intelligent transportation management and information systems (particularly those combining both live and archive data), develop improved evaluation techniques that will match transportation applications and data in speed and scale, and then thoroughly test and evaluate the results using the live and archival data sources available in the Portland State University ITS lab.This project is a collaboration between faculty, staff and students in the Data and Information Management Laboratory (http://datalab.cs.pdx.edu/) of the Computer Science Department and in the Intelligent Transportation Systems Laboratory (http://www.its.pdx.edu/) of the Civil & Environmental Engineering Department in the Maseeh College of Engineering and Computer Science at Portland State University.

交通拥堵以及相关的延误和经济成本是引起重大关注的根源。在过去的二十年中，在美国，汽车里程乘坐乘用车增长了44％，但英里的州际公路上升了不到8％！作为回应，运输部门通过使用自适应坡道电表和交通信号以及扩展的交通信息系统等工具，朝着智能运输管理迈进。用于智能运输管理的许多数据都以数据流的形式进行，例如电感环探测器数据，公共汽车上的自动车辆位置（AVL）系统以及实时交通信号数据。该项目研究了用于智能运输系统（ITS）的数据流管理系统（DSMS）的使用（ITS）。当前的DSMS技术不足以适合其应用；它的数据无序，肮脏且爆发，可以从广泛的来源（嵌入式探测器，活动车辆，被动反应器）到达。此外，必须将实时数据与存档的历史数据和其他类型的信息源进行比较。除了当前对时间聚集的关注外，其数据还需要空间和潜在的时空聚集。 Finally, any DSMS that processes ITS data must scale to thousands or tens of thousands of simultaneous queries.The goals of this research are to extend the NiagaraST stream-processing system to accommodate queries that arise in intelligent transportation management and information systems (particularly those combining both live and archive data), develop improved evaluation techniques that will match transportation applications and data in speed and scale, and then thoroughly test and evaluate the results using the live and波特兰州立大学提供的档案数据来源。该项目是计算机科学系的数据和信息管理实验室（http://datalab.cs.pdx.edu/）的教职员工和学生之间的合作波特兰州立大学科学。