Comparative Analysis Of Various Travel Time Estimation Algorithms To Ground Truth Data Using Archived DataChristopher M. Monsere

Research Assistant ProfessorDept of Civil and Environmental EngineeringPortland State University

Sirisha Kothuri, Kristin Tufte, Robert L. Bertini,

School of Urban & Public AffairsPortland State University

Incorporating Freight Performance Measures in a Regional Transportation Data Archive

Christopher M. MonsereRobert L. Bertini Zachary Horowitz Kristin A. Tufte

Department of Civil & Environmental EngineeringIntelligent Transportation Systems Laboratory

Portland State University

NATMECJune 7, 2006

Minneapolis, Minnesota

Photo J. Fischer

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Outline

Brief description of existing archiveExisting performance measurementPossible freight data sourcesSome resultsNext steps

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PORTALPSU Designated as Regional Archive Center

PSU participates in regional ITS committee—TransPort

PSU designated as regional center for collecting, coordinating and disseminating variable sources of transportation data and derived performance measures.

Bi-state: Oregon and Washington

May expand statewide.

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PORTAL Architecture Regional ITS Data Sources

98 CCTV Cameras 19 Variable Message

Signs (VMS) 485 Inductive Loop

Detectors 135 Ramp Meters Weather data TriMet Automatic Vehicle

Location (AVL) System and Bus Dispatch System (BDS)

Extensive Fiber Optics Network

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Data FundamentalsWhat Data Do We Collect?

20-second Intervals Freeway

Mainline Count Occupancy Time mean speed

20-second Intervals Freeway On-ramps Count

Hourly @PDX Temperature Precipitation

Every four hours @PDX Weather descriptor (e.g., clear, mist,

light rain, rain, fog, light snow)

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Data FundamentalsPerformance Measures We Compute

Segment Length

Vehicle Miles Traveled (VMT) = Count Segment Length

Travel Time = Segment Length Speed

Free Flow Travel Time = Segment Length Free Flow Speed

Vehicle Hours Traveled = Count Travel Time

Delay = Travel Time − Free Flow Travel Time

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Data Analysis and VisualizationHomepage

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Data Analysis and VisualizationContour Plots: Speed

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Data Quality Popup

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Data Analysis and VisualizationTime Series Plots: Volume

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Data Analysis and VisualizationGrouped Data Plots: Speed

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Performance Reports

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Monthly Reports

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Mapping and Spatial AnalysisSpeed by Month

Average Evening Peak Speed (5PM-6PM)

July 2005 Dec 2005

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Mapping and Spatial AnalysisTravel Time Reliability

Point to Point Off-Peak Travel Time Reliability (I-5 N)

Point to Point Peak-Hour Travel Time Reliability (I-5 N)

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Incident Contour Plot

Incident on I-205N at the off ramp for Hwy 212/214. A log truck rear-ended a truck carrying nursery stock; two cars also involved. Incident lasted just over 4 hours.

11/15/2005 Northbound I-205

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Incident Contour Plot

11/15/2005 Southbound I-205

Incident on I-205 Southbound.

One lane closed.

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PORTALPotential Freight Data Sources

Vehicle classificationContinuous - short and long (detector stations)Fixed classification sites

Weigh-in-motion Truck monitoring (AVI)

CorridorPoint

Spot vehicle classifications

Port of Portland / METRO / ODOT / WsDOT truck count effortOther traffic study counts

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Potential Freight Data Weigh-in-motion and Truck AVI

23 sites 1.5 years of data stored, not yet archived

Researching its usefulness

AVI tag matching projectMerging with other data

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Potential Freight Data Spot vehicle classifications

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Potential Freight Data Vehicle classification

Current firmware/software not configured to detect length PSU MTIP grant to help fix

Instead, uses vehicle classification algorithm for single loop Wang and Nihan– occupancy, count, speed estimation factor– a number of assumptions

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Potential Freight Data Vehicle classification

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Potential Freight Data Vehicle classification

Compare Wang and Nihan estimates Manual counts Video processed

Site selection (4) Existing CCTV and loops in same field of view Detector quality Trucks, no congestion Ability to request PTZ

Manual count Short (<39 ft), Long (>39 ft) Timer based count of video

Autoscope 8.1 RackVision analysis of DVR data

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Potential Freight Data Vehicle classification

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Potential Freight Data Vehicle classification

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Next steps and Concluding Remarks

Continue to explore adding freight data to archive

Vehicle classification WIM data AVI tag

Data quality?? Web interface is expanding use of

archived data Increasing awareness of the value of

these systems Provides decision support for

transportation officials in the region

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Acknowledgments PORTAL Team: Kristin Tufte, James Rucker,

Spicer Matthews, Jessica Potter, Sue Ahn, Sirisha Kothuri, Andy Delcambre, Tim Welch, Steve Hansen, Andy Rodriguez, Andrew Byrd

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

Council

Visit PORTAL online at:http://portal.its.pdx.edu

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Thank You!

Questions?