1. Implementing the ITS Archive Data User Service in Portland, Oregon Robert L. Bertini Andrew M. Byrd Thareth Yin Portland State University IEEE 7 th Annual Conference on Intelligent Transportation Systems Washington D.C.  October 4, 2004

2. 2 Objectives  Introduction to Data Archiving Project  National ITS Architecture and ADUS  Introduce PORTAL (Portland Transportation Archive Listing)  Describe Architecture  Describe Database Processing and Storage  Review Online Interface Features  Conclusions and 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) is Born 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 Data Resources Potential Archiving Applications  Traffic Surveillance  Fare/Toll Systems  Incident Management  Traffic Video  Environmental  CVO  Traffic Control  Highway/Rail  Emergency Response ITSDataArchivesITSDataArchives  Performance Monitoring National reporting Performance-based planning Evaluations Public Reactions  Long Range Planning TRANSIMS IDAS Four step models Transit routes  Operations Planning Incident management ER deployment Signal timing Transit service  Travel Time Forecasting Customized route planning ATIS Advisories  Other Stakeholder Functions Safety Land use Air quality Maintenance management

7. 7 PORTAL  Purpose: Implement the U.S. National ITS Architecture’s Archived Data User Service for the Portland metropolitan region  Cooperation with Various Agencies –Oregon Department of Transportation –Metro (Portland’s regional planning agency) –The City of Portland –TriMet (Portland’s regional transit agency)

8. 8 PORTAL PSU is the Designated 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.  Portland ITS data is warehoused at raw and aggregate levels.  An online user interface for extracting relevant performance measures in real time and historical data has been implemented.

9. 9 Portland’s Regional Infrastructure  77 CCTV Cameras  18 Variable Message Signs (VMS)  436 Inductive Loop Detectors  118 Ramp Meters  TriMet Automatic Vehicle Location (AVL) System and Bus Dispatch System (BDS)  Extensive Fiber Optics Network

10. 10 Portland ADUS Data Sources Current  Oregon Department of Transportation (ODOT)  Freeway loop detector data  Incident data (future)  National Oceanic and Atmospheric Administration (NOAA)  Weather data Future  Portland Metropolitan Transit Agency (Tri-Met)  Automatic vehicle location (AVL) poll data  Bus dispatch system (BDS) data  City of Portland  Traffic signal count/speed data  Washington State Department of Transportation (WSDOT)  ODOT Weigh-in-motion data

11. 11 PORTAL Architecture Fiber Optic Data Connection Rich Johnson, City of Portland

12. 12 PORTAL Architecture Data Flows

13. 13 PORTAL Architecture Database Back End/ Web Front End  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

14. 14 PORTAL Database Processing and Storage  Raw Data Storage  Useful for research  Large data sets  Previously difficult to manage and organize  20-second loop detector data collected  Volume, Speed, and Occupancy collected indefinitely  Transferred via fiber optic cable from ODOT TMOC to PSU relational database

15. 15 PORTAL Database Processing and Storage  Data Aggregation  5 minute and 1 hour aggregates  Performed on 20-second data every night at 3:00 am  Data appended to a table for relevant month  Flow, speed and occupancy characteristics retained  VMT, VHT, travel time, delay added to 5-min and 1 hour tables  Useful for research and planning purposes

16. 16 PORTAL Data Fidelity Quality Control  PORTAL uses Daily Statistics Algorithm (DSA) developed by the California PeMS database  DSA identifies and marks suspect or erroneous errors of four types Type 1: Occupancy and flow are mostly zero Type 2: Non-zero occupancy and zero flow Type 3: Very high occupancy Type 4: Constant occupancy and flow

17. 17 Feedback Your Opinions and Suggestions Please let us know what you think about existing or future:  www.portal.pdx.edu