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Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU Hau Hagedorn OTREC Dean Deeter

Survey of Best Practices in Real Time Travel Time Estimation and Prediction. Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU Hau Hagedorn OTREC Dean Deeter Athey Creek Consultants. Outline. Real Time Estimates Data Collection Approaches Current Travel Time Practices Case Studies

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Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU Hau Hagedorn OTREC Dean Deeter

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  1. Survey of Best Practices in Real Time Travel Time Estimation and Prediction Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU Hau Hagedorn OTREC Dean Deeter Athey Creek Consultants

  2. Outline • Real Time Estimates • Data Collection Approaches • Current Travel Time Practices • Case Studies • Summary • Lessons Learned

  3. Real-Time Travel Time Estimates • FHWA policy • Errors up to 20% acceptable • Dissemination • Websites • Dynamic Message Signs (DMS) • Cell Phones & PDA’s • In Vehicle Navigation • Radio Broadcasts

  4. Data Collection Approaches • Fixed Detection of Volume & Occupancy • High cost approach • Inductive Loops or Radar Sensors Inductive Loop Detector

  5. Fixed Detection of Speed • Low cost approach • Allows calculation of travel times easily • Lacks traffic counting capability

  6. Direct Detection of Travel Times • Toll Infrastructure • Accuracy is high Toll Tag Source: http://fastrak.sandag.org/images/ftrans.jpg Toll Plaza Source: bata.mtc.ca.gov/tolls/fastrak.html

  7. Data Fusion Approach • Private Vendors • Traffic.com • Inrix • Fusion of data • Fixed Sensors • Loops • Mobile Sensors • Fleets • Cell phones Source: www.traffic.com

  8. Current Travel Time Practices September 15, 2006 = Provide Travel Times (25) = Plans to Provide Travel Times (17) D.C. Puerto Rico Alaska Hawaii

  9. Portland, Oregon • Data Collection • ~ 500 Dual Loop Detectors • Speed, count, occupancy every 20 seconds • Travel Time Estimation • Travel times from speeds & segment lengths • Midpoint Algorithm with ODOT influence areas for each detector • Currently reported only on 3 DMS Loop Detector Locations Travel Times on DMS

  10. Seattle, Washington • Data Collection • Single Loop Detectors • Record Occupancy, Volume • Travel Time Estimation • Speeds from occupancy • Travel times from segment lengths & speeds • Includes historical travel times • Reported on DMS & web • Accuracy > 90%

  11. Minneapolis – St. Paul, Minnesota • Data Collection • Single Loop Detectors • Record Occupancy, Volume • Travel Time Estimation • Speeds from occupancy • Travel times from segment lengths & speeds • Modified Midpoint Algorithm • Reported on DMS & web • Generally accurate except in transition conditions

  12. Chicago, IL • Data Collection • Tolled Facilities • Toll tag readers & RTMS • Travel Time Estimation • Algorithm – Toll Tag + RTMS • Posted on DMS & Web • Data Collection • Non -Tolled Facilities • Loop Detectors (Occ & Vol) • Travel Time Estimation • Speeds from Occ, travel times from speeds and segment lengths

  13. San Francisco, Bay Area • Data Collection • Loop Detectors • AVI Toll Tag Readers • Spot Speed Sensors • Travel Time Estimation • Algorithm fuses data from three sources • Estimation done by private vendor • Dissemination through DMS, web and 511 system

  14. Atlanta, Georgia • Data Collection • Video Detection System Cameras • Record speed & volume • Travel Time Estimation • Travel times calculated from average speeds • Travel times estimated separately for main line and HOV lanes • Dissemination through DMS, web

  15. Summary • Common approaches for speed/travel times • Loop detectors • Minnesota, Portland, Seattle, Wisconsin • Proven technology • Detector spacing is important • AVI Toll Tags • Houston, Illinois, San Francisco, Houston • Number & spacing of tags influences accuracy • Considered reliable

  16. Speed Info Sensors • North Carolina, San Francisco • Use Doppler technology to measure speed • Reliability during adverse weather conditions is of concern • Private Approaches • Illinois • Fuse data from different sources • Offers potential with limited in field new deployment of systems

  17. Travel Time Calculations • Agency developed – In house • Seattle, Portland, Twin cities - MN • Midpoint Algorithm commonly used • State agencies are responsible for estimation and dissemination • Contractor developed • Illinois Tollway, Bay Area • Benefit derived from contractor experiences in multiple states • Maintenance and updating of proprietary algorithm performed by contractor • Estimation and dissemination of information either by agency or contractor

  18. Lessons Learned • Decision factors for travel time calculations • Travel Time reporting needs • Ownership and responsibility of data collection, equipment & algorithms • Data collection and calculation approach • Other Considerations • Cost • Reliability & accuracy • Ease of use

  19. Acknowledgements • Mark Hallenback • Galen McGill

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