Educational Applications of the Portland Oregon Regional Archived Data User Service ADUS

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Educational Applications of the Portland Oregon
Regional Archived Data User Service (ADUS)

• Robert L. Bertini
• Steven Hansen
• Portland State University
• ASEE Annual Meeting
• June 14, 2005
• 700 815 a.m.

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Outline

• What is PORTAL?
• National ITS Architecture and Archived Data User
  Service (ADUS)
• PORTAL (the Portland Transportation Archive
  Listing)
• Describe Architecture
• Describe Database Processing and Storage
• PORTAL Educational Applications
• Next Steps

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IntroductionGuiding Principles
Data are too valuable to only use once.
Management of the transportation system cannot
be done without knowledge of its performance.
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Archived Data User Service (ADUS) ITS
Architecture 1999

• USDOT Vision for ADUS
• Improve transportation decisions through the
  archiving and sharing of ITS generated data.
• Principles of ADUS to Achieve Vision
• All ITS deployments should consider data
  archiving
• Archive data to maximize integration with other
  data sources and systems
• Archive data in a way that eases retrieval for
  those with access
• Provide information that is integral to
  transportation practice

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Portland Transportation Archive Listing
(PORTAL)PSU Designated as Regional Archive Center

• Through regional cooperation, Portland State
  University is the regional center for collecting,
  coordinating and disseminating variable sources
  of transportation data and derived performance
  measures.

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PORTAL Architecture Regional ITS Data Sources

• 98 CCTV Cameras
• 19 Variable Message Signs (VMS)
• 485 Inductive Loop Detectors
• 135 Ramp Meters
• Weather data stations
• TriMet Automatic Vehicle Location (AVL) System
  and Bus Dispatch System (BDS)
• Extensive Fiber Optics Network

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PORTAL Architecture Data Flows
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PORTAL Architecture Database and Web Servers

• Database Back End
• SQL relational database
• 200 MB per day of raw data
• 75 GB per year
• High capacity disk array
• 5 MB compressed for download
• Off-site backup server
• Daily backups
• Uninterruptible power
• Web-based interface
• Easily accessible
• User-friendly
• PHP language

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Data Analysis and VisualizationHomepage
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Data Analysis and VisualizationContour Plots
Speed
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Data Analysis and VisualizationTime Series
Plots Volume
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Data Analysis and VisualizationOblique Plots
Volume
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Weather
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Data Fidelity Algorithm

• Detector Status Types
• Disabled
• Communication Error
• Data Stream Failure
• No Activity
• OK
• Suspect
• Soft Fail
• Hard Fail

Communication Errors
Good Readings
Bad Readings
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Data Fidelity
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Data Analysis and VisualizationGrouped Data
Plots Speed
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Daily Dashboard
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Monthly Reports
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Monthly Reports
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Performance Reports
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Performance Reports
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ApplicationsTravel Time Reliability
Point to Point Off-Peak Travel Time Reliability
(I-5 N)
Point to Point Peak-Hour Travel Time Reliability
(I-5 N)
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ApplicationsCongestion Definitions
Buffer Index
Travel Time Index
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Next StepsExpanding Archive Functionality

• Data from
• TriMet
• Washington State DOT
• City of Portland
• ODOT WIM Data
• Additional processing tools and performance
  measures

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Acknowledgments

• Andy Delcambre
• Spicer Matthews
• Andrew Byrd
• Andy Rodriguez
• Steve Hansen
• National Science Foundation CAREER Award Grant
  No. 0236567
• Oregon Department of Transportation
• City of Portland
• TriMet
• Portland State University
• Oregon Engineering and Technology Industry
  Council

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FeedbackYour Opinions and Suggestions

• Please let us know what you think about existing
  or future
• Data sources
• Performance measures
• Data visualizations and summaries
• Contact us
• bertini_at_pdx.edu
• http//portal.its.pdx.edu
• Sign up for an account!