Exploiting Live Plus Archive Data for Intelligent Transportation Systems

交通拥堵及其造成的延误和经济成本是一个令人严重关切的问题。在美国，在过去的20年里，乘用车行驶的车辆里程增长了44%，但州际公路里程增长了不到8%！作为回应，交通部门正在通过使用自适应匝道仪表和交通信号等工具，以及扩展的交通信息系统，向智能交通管理迈进。可用于智能交通管理的大部分数据是以数据流的形式存在的，例如感应式环路检测器数据、公交车上的自动车辆定位(AVL)系统和实时交通信号数据。当前的直接序列监测系统技术不足以满足其应用；其数据是无序的、肮脏的和突发的，并且可以从各种各样的来源(嵌入式探测器、主动车辆、被动应答器)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。除了目前对时间聚合的关注外，其数据还需要空间和潜在的时空聚合。最后，任何处理其数据的DSM都必须扩展到数千或数万个同时查询。本研究的目标是扩展NiagaraST流处理系统以适应智能交通管理和信息系统中出现的查询(特别是那些将实时数据和归档数据结合在一起的查询)，开发改进的评估技术以在速度和规模上匹配交通应用和数据，然后使用波特兰州立大学其实验室提供的实时和归档数据源来彻底测试和评估结果。该项目是教职员工之间的合作，数据与信息管理实验室(计算机科学系的http://datalab.cs.pdx.edu/))和智能交通系统实验室(土木工程与工程学院的http://www.its.pdx.edu/)；波特兰州立大学马塞工程与计算机科学学院环境工程系。

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.