2007 ITE District 6 Annual Meeting July 17, 2007 Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU...

transcript

2007 ITE District 6 Annual Meeting July 17, 2007

Sirisha KothuriKristin TufteRobert L. BertiniPSU

Hau HagedornOTREC

Dean DeeterAthey Creek Consultants

Survey of Best Practices in Real Time Travel Time Estimation and Prediction

Outline

• Real Time Estimates

• Data Collection Approaches

• Current Travel Time Practices

• Case Studies

• Summary

• Lessons Learned

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

• Fixed Detection of Volume & Occupancy– High cost approach – Inductive Loops or Radar Sensors

Data Collection Approaches

Inductive Loop Detector

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

• Direct Detection of Travel Times– Toll Infrastructure– Accuracy is high

Toll Plaza

Source: bata.mtc.ca.gov/tolls/fastrak.html

Toll Tag

Source: http://fastrak.sandag.org/images/ftrans.jpg

• Data Fusion Approach– Private Vendors

• Traffic.com• Inrix

– Fusion of data • Fixed Sensors

– Loops

• Mobile Sensors– Fleets– Cell phones

Source: www.traffic.com

Current Travel Time Practices

Alaska

Hawaii

PuertoRico

= Provide Travel Times (25)

= Plans to Provide Travel Times (17)

September 15, 2006

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

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%

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

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

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

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

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

– 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

• 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

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

Acknowledgements• Mark Hallenback

• Galen McGill

2007 ITE District 6 Annual Meeting July 17, 2007 Sirisha Kothuri Kristin Tufte Robert L. Bertini PSU...

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