1. Experience Implementing PORTAL: Portland Transportation Archive Listing Andrew M. Byrd Andy Delcambre Steve Hansen Portland State University TransNow 2 nd Annual Conference Portland, Oregon  November 19, 2004

2. 2 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 Applications  Next Steps

3. 3 Introduction Guiding Principles “Data are too valuable to only use once.”

4. 4 Introduction Guiding Principles “ Management of the transportation system cannot be done without knowledge of its performance.”

5. 5 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

6. 6 Archived Data User Service (ADUS) Implementing a Successful Data Archive A Few Examples of Existing Systems  California PeMS - only statewide system  Puget Sound (WSDOT/TRAC) - started small and successfully expanded  San Antonio, TX TransGuide, Datalink system Cooperation with Various Agencies  Oregon Department of Transportation  Metro (Portland’s regional planning agency)  The City of Portland  TriMet (Portland’s regional transit agency)

7. 7 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.

8. 8 PORTAL Architecture Regional ITS Data Sources  77 CCTV Cameras  18 Variable Message Signs (VMS)  436 Inductive Loop Detectors  118 Ramp Meters  Weather data stations  TriMet Automatic Vehicle Location (AVL) System and Bus Dispatch System (BDS)  Extensive Fiber Optics Network

9. 9 PORTAL Architecture Inductive Loop Detectors  Most commonly used data source  Work like a large metal detector  Controllers provide three data fields: volume (count), speed (average), and occupancy (% time a vehicle is over the sensor)  This data is aggregated at the loop controllers to 20 second resolution  Most frequently found at entrance ramps – one original use was for the automated control of ramp metering.

10. 10 PORTAL Architecture Inductive Loop Detectors Inductive Loop Sensors (detectors) HOVLane Station Ramp

11. 11  The loop detector system was put into place by ODOT - our portion of the project does not install or maintain sensors at their Transportation Management Operations Center  Loop detector data is used by ODOT for real-time operations – a minute to minute view of the regional transportation network  We are archiving the high resolution data that ODOT and others collect but do not retain after immediate use PORTAL Architecture Inductive Loop Detectors

12. 12 PORTAL Architecture Fiber Optic Data Connection Rich Johnson, City of Portland

13. 13 PORTAL Architecture Data Flows

14. 14  We are retaining the data in a PostgreSQL database  Data is gathered every 20 seconds and inserted into the database  Every night, the preceding day’s 20 second data is aggregated to 5 minute and 1 hour resolution  Additional analysis is performed on the original three fields - we calculate VMT, VHT, delay, and travel time PORTAL Architecture Database and Web Servers

15. 15 PORTAL Architecture Database and Web Servers  VMT = segment length * volume  Travel time = segment length / speed  VHT = travel time * volume  Delay = travel time – free flow travel time (60 mph)

16. 16 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

17. 17 PORTAL Architecture Database and Web Servers  Users have the ability to:  sample raw data  store data permanently in accordance to their specifications  use aggregate data at desired resolutions  Outputs  onscreen table  plotted on relevant graphs  downloaded on comma separated value (CSV) format for permanent offline storage

18. 18 Data Analysis and Visualization Homepage

19. 19 Data Analysis and Visualization Contour Plots: Speed

20. 20 Data Analysis and Visualization Time Series Plots: Volume

21. 21 Data Analysis and Visualization Grouped Data Plots: Speed

22. Metro (the Portland regional Metropolitan Planning Organization) uses a typical four-step computer modeling process to predict future transportation needs The final step of modeling (trip assignment) requires a function relating volume to delay for finding equilibrium on the highway network Capacity information and volume-delay relationships for segments of Oregon Highway 217 west of Portland established from archived loop data Applications Travel Demand Modeling

25. Archived data have also been used to produce regional performance measures, allowing better understanding of the transportation network Average speed maps show visually the average velocity of vehicles at different points on the highway system during peak periods Congestion frequency charts show how often congestion occurs at each time of day at different points in the transportation network Applications Performance Measures

26. Applications Regional Peak Period Speed Map

27. Applications Congestion Frequency Charts Time of day Percent of time congestion occurs Congestion frequency on I-5 South at Barbur Boulevard

28. 28 Next Steps Expanding Archive Functionality  Data from:  TriMet  Washington State DOT  City of Portland  ODOT WIM Data  Additional processing tools and performance measures

29. Acknowledgments  National Science Foundation  Oregon Department of Transportation  City of Portland  TriMet  Portland State University  Oregon Engineering and Technology Industry Council

30. 30 Feedback Your 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@pdx.edu http://portal.its.pdx.edu Sign up for an account!