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

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

5 National ITS Architecture Major Components  Travel and Traffic Management  Public Transportation Management  Electronic Payment  Commercial Vehicles Operations  Emergency Management  Advanced Vehicle Safety Systems  Information Management  Maintenance and Construction Management

6 Overview: Relational Diagram of ITS Architecture Source: Guidelines for Developing ITS Data Archiving Systems

7 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

8 Archived Data User Service (ADUS) is Born ADUS Standards  Operational Data Control: by managing operations data integrity  Data Import and Verification: through historical data  Automatic Data Historical Archive: with a permanent data archive  Data Warehouse Distribution: which integrates the planning, safety, operations and research communities and processes data for these communities  ITS Community Interface: by providing a common interface to all ITS users for data products specification and retrieval

9 Who Can Use Archived ITS Data Stakeholders  Transportation Planning  Transportation System Monitoring  Air Quality Analysis  MPO/State Freight and Intermodal Planning  Land Use/Growth Management Planning  Transportation Administrators and Policy Analysis  Traffic Management  Transit Management  Construction and Maintenance  Safety Planning and Administration  Commercial Vehicle Operations  Emergency Management  Transportation Research  Private Sector

10 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

11 ADUS Architecture: 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 Lessons Learned from Existing Systems  Begin with a single data source  Provide data through the web or CD subscription  Create a user friendly interface  Save raw data  Make aggregate data available to users  Implement data quality control measures  Create adequate documentation of system and metadata

12 Portland Regional ADUS: PORTAL (Portland Transportation Archive Listing)

13 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)

14 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.

15 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

16 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

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

18 PORTAL Architecture Data Flows

19 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

20 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

21 PORTAL Database Processing and Storage  Data Aggregation  5 and 15 minute 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

22 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

PORTAL Web Interface Homepage

24 PORTAL Database Processing and Storage  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

Data Analysis and Visualization Contour Plots: Speed

Data Analysis and Visualization Time Series Plots: VHT

Data Analysis and Visualization Data Fidelity

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

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

31 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: