1 arterial performance measurement mathew berkow, michael wolfe, christopher monsere and robert l....

1Arterial Performance MeasurementArterial Performance Measurement

Mathew Berkow, Michael Wolfe, Christopher Monsere and Mathew Berkow, Michael Wolfe, Christopher Monsere and Robert L. BertiniRobert L. BertiniIntelligent Transportation Systems LaboratoryIntelligent Transportation Systems LaboratoryMaseeh College of Engineering and Computer ScienceMaseeh College of Engineering and Computer SciencePortland State UniversityPortland State University

Arterial Performance Measurement Using Arterial Performance Measurement Using Transit AVL and Signal System DataTransit AVL and Signal System Data

IntroductionIntroduction

Develop an automated way to report speeds, travel times and performance measures using: Existing ITS signal infrastructure Automatic Vehicle Locator (AVL) data

Expand PORTAL to include arterial data

Develop an automated way to report speeds, travel times and performance measures using: Existing ITS signal infrastructure Automatic Vehicle Locator (AVL) data

Expand PORTAL to include arterial data

PORTAL -- Regions ADUSPORTAL -- Regions ADUSPORTAL -- Regions ADUSPORTAL -- Regions ADUS

What’s in the PORTAL Database?What’s in the PORTAL Database?

Loop Detector DataLoop Detector Data20 s count, lane occupancy, 20 s count, lane occupancy, speed from 500 detectors speed from 500 detectors

(1.2 mi spacing) (1.2 mi spacing)

Incident DataIncident Data140,000 since 1999140,000 since 1999

Weather DataWeather DataEvery day since 2004Every day since 2004

VMS DataVMS Data19 VMS since 199919 VMS since 1999

DaysDaysSince July 2004Since July 2004About 300 GBAbout 300 GB

4.2 Million Detector 4.2 Million Detector IntervalsIntervals

Bus DataBus Data1 year stop level data1 year stop level data

140,000,000 rows140,000,000 rows

001703

WIM DataWIM Data22 stations since 200522 stations since 2005

30,026,606 trucks30,026,606 trucks

Crash DataCrash DataAll state-reported crashes All state-reported crashes

since 1999 - ~580,000since 1999 - ~580,000

What’s Behind the Scenes?What’s Behind the Scenes?

Database ServerDatabase ServerPostgreSQL Relational Database PostgreSQL Relational Database Management System (RDBMS)Management System (RDBMS)

StorageStorage2 Terabyte Redundant Array 2 Terabyte Redundant Array of Independent Disks (RAID)of Independent Disks (RAID) Web InterfaceWeb Interface

Selected Arterial Performance MeasuresSelected Arterial Performance MeasuresSelected Arterial Performance MeasuresSelected Arterial Performance Measures

TABLE 1 SELECTED ARTERIAL PERFORMANCE MEASURES

Metric Measurement Interval Location Maximum Speed

per Vehicle per Person

per Distance per Time

(cycle, 15 min, hour, day)

per Lane per Lane Group per Approach per Segment per Facility

per Area

Average Speed Speed Indexa Density Running Time Travel Time Travel Time Variance Average Delay Maximum Delay Queue Length Platoon Ratio Number of Stops Signal Failure Duration of Congestion per Day Number of Incidents per Day/Peak Period Duration Incidents per Event Nonrecurring Delay aRatio of average speed to posted speed.

Inspiration – Signal System Data OnlyInspiration – Signal System Data OnlyInspiration – Signal System Data OnlyInspiration – Signal System Data Only

Inspiration – Bus AVL System Data OnlyInspiration – Bus AVL System Data OnlyInspiration – Bus AVL System Data OnlyInspiration – Bus AVL System Data Only

SR 217

Powell Blvd.

Powell Blvd. Corridor StudyPowell Blvd. Corridor StudyPowell Blvd. Corridor StudyPowell Blvd. Corridor Study

Study Corridor

Four-lane arterial 3 Miles

East-West 50,000 ADT

TriMet’s Bus Dispatch System (BDS)

On-BoardComputer

DoorsLift

APC (Automatic Passenger Counter)

Overhead SignsOdometer

Signal Priority Emitters

Memory Card

RadioSystem

Garage PC’s

Radio AntennaGPS Antenna

Navstar GPS Satellites

Control Head

BDS DataR

9 01NOV2001 8:53:32 8:49:15 8:53:28 285 0 1120 4964 0 0 0 0 0 21 41 10558.58 7644468 676005

9 01NOV2001 8:55:00 8:51:41 8:54:46 285 0 1120 4701 4 0 0 0 1 20 50 15215.05 7649112 676328

9 01NOV2001 8:56:22 8:52:00 8:55:08 285 0 1120 4537 36 3 0 6 0 26 34 15792.35 7649674 676220

Route Number Vehicle Number Service Date Actual Leave Time Scheduled Stop Time Actual Arrive Time Operator ID Direction Trip Number Bus Stop Location

Dwell Time Door Opened Lift Usage Ons & Offs (APCs) Passenger Load Maximum Speed on

Previous Link Distance Longitude Latitude

Probe Vehicle Location Over Time

DIVISION

FRANCIS

LINCOLN

HARRISON

3-D Speed Contour

Distance

3-D Speed Contour

Conceptual Bus

Hypothetical Bus – run without stops or passenger activities (no dwell)

Pseudo Bus – travel at maximum achieved speed record within link

Modified Pseudo Bus – travel at maximum achieved speed with additional time spent at stops

7:07:00 AM 7:12:00 AM 7:17:00 AMTime

Hypothetical Bus

Pseudo Bus

Modified Pseudo Bus

Ross IslandBridge

Thurs. Nov. 1, 2001

Stop circle

1:04:00 PM 1:09:00 PM 1:14:00 PMTime

Ross Island

Bridge

Test VehicleBusHypothetical BusPseudo BusModified Pseudo Bus B

Test Vehicles vs. Buses

0:34 0:34

0:28 0:28

0:48 0:48

0:44 0:44

Test Vehicle Bus Hypo Pseudo Modify

Entire Corridor

On the Bridge

3rd Quartile

1st Quartile

Median

Test Vehicle vs. Pseudo

Test Vehicle Pseudo Test Vehicle Pseudo

Weekday Morning Peak 28.8 37.2 0.77 5:34 4:17 1.30

Weekend Midday Off-Peak 32.0 35.8 0.89 4:36 4:23 1.05

b ttMean Travel Time

EastboundMean Speed b speed

Mean Travel Time

WestboundMean Speed b speed

Ross Island Bridge

Four-lane traffic (2 lane on both side)

No shoulders Approaches are

bottleneck No interruptions No bus stops

Test Vehicle vs. Pseudo

b ttBridge: Mean Travel Time

EastboundBridge: Mean Speed b speed

Bridge: Mean Travel Time

WestboundBridge: Mean Speed b speed

This Project: Combine Signal and Bus AVLThis Project: Combine Signal and Bus AVLThis Project: Combine Signal and Bus AVLThis Project: Combine Signal and Bus AVL

Past and Ongoing EffortsPast and Ongoing EffortsPast and Ongoing EffortsPast and Ongoing Efforts

Using stop bar detectors to generate Using stop bar detectors to generate an arterial performance mapan arterial performance map Hallenbeck, Ishimaru, Davis, KangHallenbeck, Ishimaru, Davis, Kang LiangLiang

Performance based on summation of Performance based on summation of delay componentsdelay components Liu, MaLiu, Ma Skabardonis, GeroliminisSkabardonis, Geroliminis

Intersection/Signal PerformanceIntersection/Signal Performance Sharma, Bullock, BonnesonSharma, Bullock, Bonneson Smaglik, Bullock, SharmaSmaglik, Bullock, Sharma

Signal System Data:Signal System Data:Portland’s Portland’s Detection Detection InfrastructureInfrastructure

Data AggregationData AggregationCount Station Count Station

5 min5 minOther Detector Other Detector

15 min15 min7 Day Sample7 Day Sample

Case Study: Barbur Blvd. Speed MapCase Study: Barbur Blvd. Speed MapCase Study: Barbur Blvd. Speed MapCase Study: Barbur Blvd. Speed Map

SheridanHooker

Hamilton

3rdTerwilligerBertha

I-5 Off-ramp

Park & Ride

Will It Work? To The Video….Will It Work? To The Video….Will It Work? To The Video….Will It Work? To The Video….

Detectors at Barbur and BerthaDetectors at Barbur and BerthaDetectors at Barbur and BerthaDetectors at Barbur and Bertha

Density vs. OccupancyDensity vs. Occupancy

Density = number of vehicles per distance

Occupancy = percent of time with a vehicle on the sensor

Density = Occupancy X 1/(vehicle length + sensor length)

Density = number of vehicles per distance

Occupancy = percent of time with a vehicle on the sensor

Density = Occupancy X 1/(vehicle length + sensor length)

Density = 2 vehicles / 45 feet =.044

45’12’

Density = .80 * 1 / (12 + 6) = .044

Occupancy = 80%

Flow vs. Occupancy: 5 Minute DataFlow vs. Occupancy: 5 Minute DataFlow vs. Occupancy: 5 Minute DataFlow vs. Occupancy: 5 Minute Data

Barbur & Hamilton, Northbound 2-12-07 to 2-20-07

0 10 20 30 40 50 60 70 80

Occupancy (%)

5 Minute Speed and Occupancy (at Hamilton)5 Minute Speed and Occupancy (at Hamilton)5 Minute Speed and Occupancy (at Hamilton)5 Minute Speed and Occupancy (at Hamilton)

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

Occupancy

Generate Occupancy Map From Detector DataGenerate Occupancy Map From Detector DataGenerate Occupancy Map From Detector DataGenerate Occupancy Map From Detector Data

AM Peak Speed Map From Detector DataAM Peak Speed Map From Detector DataAM Peak Speed Map From Detector DataAM Peak Speed Map From Detector Data

Bus Geolocation DataBus Geolocation DataBus Geolocation DataBus Geolocation Data

ObjectiveObjective

Develop an automated way to report SpeedsTravel timesPerformance measures

Using Existing ITS signal infrastructureAutomatic Vehicle Locator (AVL) data

Develop an automated way to report SpeedsTravel timesPerformance measures

Using Existing ITS signal infrastructureAutomatic Vehicle Locator (AVL) data

1 Year’s Worth of TriMet Data1 Year’s Worth of TriMet Data

92 Bus Line 7,600 Stop Locations 273,469 Runs 140,751,486 Stops 50 Gigabytes of Data Extensive Network Coverage Opportunity to Evaluate Multiple Routes on

Same Arterial

92 Bus Line 7,600 Stop Locations 273,469 Runs 140,751,486 Stops 50 Gigabytes of Data Extensive Network Coverage Opportunity to Evaluate Multiple Routes on

Same Arterial

Time-Space DiagramTime-Space Diagram

nalize

rsecti

Detector Limited in SpaceDetector Limited in Space

nalize

rsecti

Bus Limited in TimeBus Limited in Time

Building on Powell Blvd. StudyBuilding on Powell Blvd. StudyBuilding on Powell Blvd. StudyBuilding on Powell Blvd. Study

Begin with limited signal system data.Begin with limited signal system data.

Gather Gather archivedarchived TriMet AVL data. TriMet AVL data.

Merge two data sources to examine synergies due to Merge two data sources to examine synergies due to data fusion.data fusion.

Use AVL data to calibrate influence areas from loops.Use AVL data to calibrate influence areas from loops.

Buses Inform Detector Readings – 2/12/07Buses Inform Detector Readings – 2/12/07Buses Inform Detector Readings – 2/12/07Buses Inform Detector Readings – 2/12/07

Congestion AlgorithmCongestion AlgorithmCongestion AlgorithmCongestion Algorithm

1. Calculate the slope of each bus segment (less dwell time) which is equal to the average bus speed

2. Identify a threshold speed that will be considered congestion

3. Find consecutive segments that experience travel speeds below the threshold ‘congested’ speed

1. Calculate the slope of each bus segment (less dwell time) which is equal to the average bus speed

2. Identify a threshold speed that will be considered congestion

3. Find consecutive segments that experience travel speeds below the threshold ‘congested’ speed

Location (x+1)

Location (x)

Location (x-1)

Segment 2

Location (x+1)

Location (x)

Location (x-1)

Segment 2

Midpoint Method Using 5-Minute DataMidpoint Method Using 5-Minute DataMidpoint Method Using 5-Minute DataMidpoint Method Using 5-Minute Data

nalize

rsecti

Adjust Influence Areas ManuallyAdjust Influence Areas ManuallyAdjust Influence Areas ManuallyAdjust Influence Areas Manually

nalize

rsecti

Bus Data Confirms AdjustmentBus Data Confirms AdjustmentBus Data Confirms AdjustmentBus Data Confirms Adjustment

nalize

rsecti

Reveals Gaps in DetectionReveals Gaps in DetectionReveals Gaps in DetectionReveals Gaps in Detection

nalize

rsecti

New Occupancy Map From Combined SourcesNew Occupancy Map From Combined SourcesNew Occupancy Map From Combined SourcesNew Occupancy Map From Combined Sources

nalize

rsecti

An Improvement Over Mid-Point MethodAn Improvement Over Mid-Point MethodAn Improvement Over Mid-Point MethodAn Improvement Over Mid-Point Method

nalize

rsecti

Actual Bus Signal Max Speed X1.25

Signal + Bus

Morning Peak Midday Off-Peak Evening Peak

Average Link Travel TimesAverage Link Travel TimesAverage Link Travel TimesAverage Link Travel Times

Ground truth

ObstaclesObstacles

System Signal Detector Very Limited Aggregation Access to Real Time Data Limited Detection & Spacing

Bus Access to Real Time Data

System Signal Detector Very Limited Aggregation Access to Real Time Data Limited Detection & Spacing

Bus Access to Real Time Data

Next StepNext Step

System Signal Detector Cycle level data (Gresham, OR – SCATS)

Bus TriMet Buses Can Be Probes Extensive Network Coverage Opportunity to Evaluate Multiple Routes on

Same Arterial

System Signal Detector Cycle level data (Gresham, OR – SCATS)

Bus TriMet Buses Can Be Probes Extensive Network Coverage Opportunity to Evaluate Multiple Routes on

Same Arterial

Bus FrequencyBus FrequencyBus FrequencyBus Frequency

CTRAN – 10 Second ReportingCTRAN – 10 Second ReportingCTRAN – 10 Second ReportingCTRAN – 10 Second Reporting

Next Steps: Gresham Burnside CorridorNext Steps: Gresham Burnside Corridor

Good detector density

Highly granular data (cycle-by-cycle)

Probe data available

No direct measurement of occupancy

16’ detectors No bus data

Good detector density

Highly granular data (cycle-by-cycle)

Probe data available

No direct measurement of occupancy

16’ detectors No bus data

AcknowledgementsAcknowledgements

TransPort Members FHWA: Nathaniel Price ODOT: Galen McGill PSU & OTREC (Local Matching

Funds) City of Portland: Bill Kloos, Willie

Rotich TriMet: David Crout, Steve Callas JPACT and Oregon Congressional

Delegation ITS Lab: John Chee, Rafael

Fernandez

TransPort Members FHWA: Nathaniel Price ODOT: Galen McGill PSU & OTREC (Local Matching

Funds) City of Portland: Bill Kloos, Willie

Rotich TriMet: David Crout, Steve Callas JPACT and Oregon Congressional

Delegation ITS Lab: John Chee, Rafael

Fernandez

Next Steps: Vancouver Mill Plain Blvd.Next Steps: Vancouver Mill Plain Blvd.

Detailed Data Available Do Not Have Sample Data Yet Possible Future Project

1 arterial performance measurement mathew berkow, michael wolfe, christopher monsere and robert l....

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