Exploiting Live Plus Archive Data for Intelligent Transportation Systems

交通拥堵及其造成的相关延误和经济成本是一个令人严重关切的问题。在美国，在过去的二十年里，乘用汽车的行驶里程增长了44%，但州际公路的行驶里程增长了不到8%！对此，交通部门正在通过使用自适应匝道计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。智能交通管理中使用的大部分数据都是以数据流的形式出现的，例如感应线圈检测器数据、公交车上的自动车辆定位（AVL）系统和实时交通信号数据。本项目研究智能交通系统（ITS）中数据流管理系统（DSMS）的使用。目前的数字信号管理系统技术不足以满足智能交通系统的应用;智能交通系统的数据是无序的、肮脏的和突发的，可以来自各种各样的来源（嵌入式探测器、主动车辆、被动转发器）。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，ITS数据需要空间和潜在的时空聚合。最后，任何处理ITS数据的DSMS都必须扩展到数千或数万个并发查询。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（特别是结合实时和存档数据的那些），开发改进的评估技术，使其在速度和规模上与交通应用和数据相匹配，然后使用波特兰州立大学ITS实验室中可用的实时和存档数据源对结果进行彻底测试和评估。该项目是波特兰州立大学工程与计算机科学学院计算机科学系数据与信息管理实验室（http：//datalab.cs.pdx.edu/）和土木环境工程系智能交通系统实验室（http：//www.its.pdx.edu/）的教职员工和学生之间的合作&。

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.