multimodal its data integration and perfomance...

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Robert L. BertiniPortland State UniversityNorth American Travel Monitoring Exhibition & ConferenceSan Diego, California June 30, 2004

Multimodal ITS Data Integration and Perfomance Measurement in Portland, Oregon

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OutlineRegional VisionRegional ITS Partners and Data IntegrationWorking with Metropolitan Planning Organization

Empirical data for travel demand modelPerformance measures for annual report

ITS Data IntegrationVideo Imaging Processing and Loop Detector Data to Provide Vehicle ClassificationFreeway Incident Management Performance AssessmentUsing Bus AVL Systems and Loop Detector Data to Characterize Corridor Performance Using Bus AVL Data To Characterize Arterial Performance (Session 21)

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ITS Data IntegrationRegional Vision

Warehouse ITS data at their most raw level. Include user interface for extracting relevant performance measures in real time and historically.Through regional cooperation, Portland State University is the regional center for collecting, coordinating and disseminating variable sources of transportation data and derived performance measures.Data can be mined for operations, planning, management and research purposes for the transportation system

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Regional ITS PartnershipsData IntegrationTransPort Technical Advisory Committee

Oregon Department of Transportation City of PortlandTriMet Metro (OR)Regional Transportation Commission (WA)Washington State Department of TransportationCities (bi-state)Counties (bi-state)C-TranPort of PortlandPortland State University

Monthly coordination meetingsVoluntary project funding integrationSubcommittee Structure

DataITS ArchitectureCommunications

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75 CCTV cameras 18 variable message signs 118 ramp meters436 inductive loop detectors24 hour/day incident response with 11 vehiclesDigital archives of incident logsAVL Archives of COMET movementsExtensive fiber optic communications system

Traffic Management SystemPortland Region

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Traveler InformationPortland Region

Variable Message Signs

Traffic Cameras

Traffic Reports

www.tripcheck.com

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Integrating ITS Data Into Corridor PlanningMetro Highway 217 Corridor Study

Available archived loop detector data along key travel corridor.Possible to incorporate updated speed/travel time information into trip distribution step of Metro’s travel demand modelBackground

Metro developing corridor study for Highway 217 corridor improvementsEMME/2 travel demand modelTrip Distribution step relies on “theoretical” and “default” values

Opportunity for linking ITS data to regional transportation planning efforts.

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Supplement theoretical and default values in the trip distribution step with empirical data.Improve Bureau of Public Roads (BPR) volume/travel time functions.BPR does not take into account regional variations and dynamic changes for a particular facilityFacility characteristics and driver behavior have changed significantly over the 40 years since BPR functions were created.Recent developments in the transportation system such as ramp metering and other ITS technologies may have altered highway performance.


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Study AreaRamp metering39 detectors at 9 locationsEstimated segment capacity from loop detector data

CountOccupancyQueue discharge

Created function relating V/C ratio to travel time

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oblique 1524

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2174


Oblique Plot of Flow at Detector 1524

Maximum Flow 2170 vph

Time

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Highway 217 Volume/Delay Curve

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Regional Performance Measures Metro Performance Report 2004

Metro Performance Report – reports on progress of region including transportation system.

PSU ITS Lab generated performance measures as indicator of possible future ITS based reporting.

Sample performance data prepared in 2004:

Average Speed Map for the regional highway systemCongestion frequency charts for key corridors

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Regional Performance Measures Average Speed Map

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Regional Performance Measures Congestion Frequency

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Percent of Reported Speeds < 30mph (2001)WB I-84 Between I-205 and I-5

Time

Per

cent

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Video Imaging Processing Vehicle Classification

Regional need for truck count data on an ongoing basis.Live feed from 75 CCTV cameras into ITS Lab.Using Autoscope to extract vehicle classification data.Developing statistical sampling plan:

SpatialTemporal

Challenge is to properly re-orient cameras.Collaborating with University of Washington.

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Video Imaging Processing Vehicle Classification

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1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286

5 Minutes Unit

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nts

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icle

s/5m

inut

es)

Total Counts

Truck Counts

Testing loop only algorithm to extract truck count estimates.5 minute intervals.Uncongestedconditions.Wang-Nihanalgorithm.Developing sampling plan. Collaborating with University of Washington.

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Freeway Incident Data IntegrationIncident Management Program Assessment

Low

Medium

High

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215 208 236 269 222 234 186 242 195304 293 257

789 764803 803 917

714919 892

745

937 1009

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497445

425 462

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585 514

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547538

492

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ryFeb

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berDec

ember

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ber o

f Inc

iden

ts

Crash Stall OtherN=18920

1501 153414641417

1760

1392

16901648

1363

1788 1840

1523 Mean1576


Year 2001 Incident Inventory

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6550

79

144

50

101

36 43 35

174 181194

150 158 157172

133150

199

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66

130125

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Crashes on Wet DaysCrashes on Dry DaysWet Days1152 Crashes on Wet Days

1712 Crashes on Dry Days113 Wet days


Impact of Weather on Incident Frequency

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Ongoing Incidents 1 Day

12:00AM

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Time of Day

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41

51

61

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81In

cide

nt C

ount

Incident Time and Duration on November 18th 2002

N=83

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1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00AM

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Incidents Scheduled IR Vehicles

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Year 2001 Average Ongoing Incidents

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icle

s

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Using Bus AVL Systems and Loop Detector Data to Characterize Corridor Performance

TriMet Express Bus Routes on Interstate 5Route 95X morning peak service between Sherwood and Downtown Portland Route 96X provides morning and evening peak only service betweenSherwood and Downtown PortlandThree week I-5 experiment

Loop detector dataExpress bus AVL data (pseudo stops on freeway)

Geo-coded data from Tri-Met’s automatic vehicle location (AVL) system was used to build trip trajectories.Trajectories were overlaid onto contour plots of speed data fromODOT’s inductive loop detectors on Northbound I-5 CorridorMerging of two data sources to effectively achieve travel time estimation and validation.

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I-5 Freeway Corridor PerformanceBus AVL and Loop Detector Data

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I-5 Northbound, November 7, 2002

I-5 Freeway Corridor PerformanceLoop Detector Data

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I-5 Freeway Corridor PerformanceBus AVL Data

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0.20.40.40.50.60.60.20.2Variance

7.68.313.513.69.59.17.97.5Mean

LoopBusLoopBusLoopBusLoopBus

Nov 14 2002Nov 13 2002Nov. 7 2002Nov. 6 2002

Northbound Corridor Travel Times (min)

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Acknowledgments

Andrew Byrd, B.S., Computer ScienceThareth Yin, M.S., Civil & Environmental EngineeringMichael Rose, Master of Urban & Regional Planning

National Science FoundationOregon Department of Transportation City of PortlandTriMetPortland State UniversityOregon Engineering and Technology Industry Council

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For More InformationComments and Feedback Welcome

Thank You!

http://www.its.pdx.edu

multimodal its data integration and perfomance...

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