2007 ITE District 6 Annual Meeting July 17, 2007 Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU Hau Hagedorn OTREC Dean Deeter Athey Creek Consultants Survey of Best Practices in Real Time Travel Time Estimation and Prediction

2007 ITE District 6 Annual Meeting July 17, 2007 Outline Real Time Estimates Data Collection Approaches Current Travel Time Practices Case Studies Summary Lessons Learned

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 Fixed Detection of Volume & Occupancy –High cost approach –Inductive Loops or Radar Sensors Data Collection Approaches Inductive Loop Detector

2007 ITE District 6 Annual Meeting July 17, 2007 Fixed Detection of Speed –Low cost approach –Allows calculation of travel times easily –Lacks traffic counting capability

2007 ITE District 6 Annual Meeting July 17, 2007 Direct Detection of Travel Times –Toll Infrastructure –Accuracy is high Toll Plaza Source: bata.mtc.ca.gov/tolls/fastrak.html Toll Tag Source:

2007 ITE District 6 Annual Meeting July 17, 2007 Data Fusion Approach –Private Vendors Traffic.com Inrix –Fusion of data Fixed Sensors –Loops Mobile Sensors –Fleets –Cell phones Source:

2007 ITE District 6 Annual Meeting July 17, 2007 Current Travel Time Practices Alaska Hawaii Puerto Rico D.C. = Provide Travel Times (25) = Plans to Provide Travel Times (17) September 15, 2006

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 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%

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 –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

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 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

2007 ITE District 6 Annual Meeting July 17, 2007 Acknowledgements Mark Hallenback Galen McGill