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latte


Faculty: David Maier, Robert Bertini (CEE), Kristin Tufte
Students: Jin Li
Web Link: latte

Description 

 
Traffic congestion and the associated delay and economic costs it causes are a source of significan 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. 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).
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 of the Computer Science Department and the Intelligent Transportation Systems 
Laboratory of the Civil & Environmental Engineering Department in the Maseeh College of Engineering and
 Computer Science at Portland State University. 


This project is funded by the National Science Foundation through grant IIS-0612311 "Exploiting
 Live plus Archive Data for Intelligent Transportation Systems." Any opinions, findings, and conclusions or
 recommendations expressed in this material are those of the author(s) and do not necessarily reflect
 the views of the National Science Foundation.

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