1 natmec 2008 christopher monsere robert l. bertini, mathew berkow, and michael wolfe intelligent...
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1NATMEC 2008NATMEC 2008
Christopher Monsere Christopher Monsere Robert L. Bertini, Mathew Berkow, and Michael WolfeRobert L. Bertini, Mathew Berkow, and Michael WolfeIntelligent Transportation Systems LaboratoryIntelligent Transportation Systems LaboratoryMaseeh College of Engineering and Computer ScienceMaseeh College of Engineering and Computer SciencePortland State UniversityPortland State University
Towards Incorporating Arterial Performance Towards Incorporating Arterial Performance Quality in the PORTAL Archived Data User Quality in the PORTAL Archived Data User ServiceService
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OutlineOutline
What is PORTAL? Arterial performance Our prototype using:
Existing traffic signal infrastructureProbe (bus) geolocation data
Did it work? Next steps
What is PORTAL? Arterial performance Our prototype using:
Existing traffic signal infrastructureProbe (bus) geolocation data
Did it work? Next steps
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PORTAL -- Region’s ADUSPORTAL -- Region’s ADUSPORTAL -- Region’s ADUSPORTAL -- Region’s ADUS
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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
001497
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
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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
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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.
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Inspiration – Signal System Data OnlyInspiration – Signal System Data OnlyInspiration – Signal System Data OnlyInspiration – Signal System Data Only
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Inspiration – Bus Geolocation Data OnlyInspiration – Bus Geolocation Data OnlyInspiration – Bus Geolocation Data OnlyInspiration – Bus Geolocation Data Only
Powell Blvd.
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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
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Past and Ongoing Efforts – Signal DataPast and Ongoing Efforts – Signal DataPast and Ongoing Efforts – Signal DataPast and Ongoing Efforts – Signal Data
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
Oregon Historical Society
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DDDD
Signal System Data:Signal System Data:Portland’s Portland’s Detection Detection InfrastructureInfrastructure
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
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Case Study: Barbur Blvd. Speed MapCase Study: Barbur Blvd. Speed MapCase Study: Barbur Blvd. Speed MapCase Study: Barbur Blvd. Speed Map
SheridanHooker
Hamilton
3rdTerwilligerBertha
19th
I-5 Off-ramp
30th
Park & Ride
N
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Detectors at Barbur and BerthaDetectors at Barbur and BerthaDetectors at Barbur and BerthaDetectors at Barbur and Bertha
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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
10
20
30
40
50
60
70
80
90
0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00
Time
Sp
eed
(m
ph
)
0
10
20
30
40
50
60
70
80
Occ
up
ancy
(%
)
Speed
Occupancy
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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
Hamilton
Bertha
Park & Ride
Sheridan
7:00 AM 9:00 AM
Slope =Distance /Time
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Signal Data Only SummarySignal Data Only Summary
Point Detection Detector spacing and coverage How to extrapolate measurement to link level?
Very Limited Time Aggregation 5 Minute Won’t Work!
Lack of Access to Real Time Data
Point Detection Detector spacing and coverage How to extrapolate measurement to link level?
Very Limited Time Aggregation 5 Minute Won’t Work!
Lack of Access to Real Time Data
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Past and Ongoing Efforts –Bus GeolocationPast and Ongoing Efforts –Bus GeolocationPast and Ongoing Efforts –Bus GeolocationPast and Ongoing Efforts –Bus Geolocation
Buses as probesBuses as probes Bertini & Tantiyanugulchai (2003)Bertini & Tantiyanugulchai (2003) Vandehey, Parks, Koonce & Vandehey, Parks, Koonce &
Bonneson (2006 working paper)Bonneson (2006 working paper) Chakroborty and Kikuchi (2004)Chakroborty and Kikuchi (2004)
Measuring & reporting Measuring & reporting congestioncongestion Hall and Vyas (2000)Hall and Vyas (2000)
Measuring network LOSMeasuring network LOS Uno, Tamura, Iida, Nagahiro and Uno, Tamura, Iida, Nagahiro and
Yamawaki (2007)Yamawaki (2007)
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TriMet Archived AVL DataTriMet Archived AVL DataTriMet Archived AVL DataTriMet Archived AVL DataR
ou
te N
o.
Ser
vice
D
ate
Lea
ve T
ime
Sto
p T
ime
Arr
ive
Tim
e
Bad
ge
Dir
ecti
on
Tri
p N
o.
Lo
cati
on
ID
Dw
ell
Do
or
Lif
t
On
s
Off
s
Est
. L
oad
Max
Sp
eed
Pat
tern
D
ista
nce
X C
oo
r.
Y C
oo
r.
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
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Powell Blvd. Corridor StudyPowell Blvd. Corridor StudyPowell Blvd. Corridor StudyPowell Blvd. Corridor Study
0
0.5
1
1.5
2
2.5
3
1:04:00 PM 1:09:00 PM 1:14:00 PMTime
Dis
tan
ce (
mile
s)
Ross Island
Bridge
Test VehicleBusHypothetical BusPseudo BusModified Pseudo Bus B
us
Hyp
o
Pseu
do
Mod
ified
Ps
eudo
Vehi
cle
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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
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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. MergeMerge two data sources to examine synergies two data sources to examine synergies
due to due to data fusiondata fusion.. Use geolocation data to calibrate influence Use geolocation data to calibrate influence
areas from loops.areas from loops.
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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
Hamilton
Bertha
Park & Ride
Sheridan
8:00 AM 9:00 AM
3.5
1.9
0
4.5
Inte
rsect
ion N
am
e
Dis
tance
(m
iles)
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Buses Inform Detector Readings – 2/15/07Buses Inform Detector Readings – 2/15/07Buses Inform Detector Readings – 2/15/07Buses Inform Detector Readings – 2/15/07
Hamilton
Bertha
Park & Ride
Sheridan
8:00 AM 9:00 AM
3.5
1.9
0
4.5
Inte
rsect
ion N
am
e
Dis
tance
(m
iles)
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Hamilton
Inte
rsect
ion N
am
e
Park & Ride
Sheridan
6:00 PM
3.5
1.9
0
4.5
Dis
tance
(m
iles)
Bertha
Midpoint Method Using 5-Minute DataMidpoint Method Using 5-Minute DataMidpoint Method Using 5-Minute DataMidpoint Method Using 5-Minute Data
5:00 PM 5:30 PM
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Adjust Influence Areas ManuallyAdjust Influence Areas ManuallyAdjust Influence Areas ManuallyAdjust Influence Areas Manually
Hamilton
Inte
rsect
ion N
am
e
Park & Ride
Sheridan
6:00 PM
3.5
1.9
0
4.5
Dis
tance
(m
iles)
Bertha
5:00 PM 5:30 PM
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Bus Data Confirms AdjustmentBus Data Confirms AdjustmentBus Data Confirms AdjustmentBus Data Confirms Adjustment
Hamilton
Inte
rsect
ion N
am
e
Park & Ride
Sheridan
6:00 PM
3.5
1.9
0
4.5
Dis
tance
(m
iles)
Bertha
5:00 PM 5:30 PM
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Reveals Gaps in DetectionReveals Gaps in DetectionReveals Gaps in DetectionReveals Gaps in Detection
Hamilton
Inte
rsect
ion N
am
e
Park & Ride
Sheridan
6:00 PM
3.5
1.9
0
4.5
Dis
tance
(m
iles)
Bertha
5:00 PM 5:30 PM
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New Occupancy Map From Combined SourcesNew Occupancy Map From Combined SourcesNew Occupancy Map From Combined SourcesNew Occupancy Map From Combined Sources
Hamilton
Inte
rsect
ion N
am
e
Park & Ride
Sheridan
6:00 PM
3.5
1.9
0
4.5
Dis
tance
(m
iles)
Bertha
5:00 PM 5:30 PM
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An Improvement Over Mid-Point MethodAn Improvement Over Mid-Point MethodAn Improvement Over Mid-Point MethodAn Improvement Over Mid-Point Method
Hamilton
Inte
rsect
ion N
am
e
Park & Ride
Sheridan
6:00 PM
3.5
1.9
0
4.5
Dis
tance
(m
iles)
Bertha
5:00 PM 5:30 PM
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The Proof?The Proof?The Proof?The Proof?
02468
1012141618
Actual Bus Max Speed Method
Signal Max Speed x 1.25
Signal + Bus
Trav
el T
ime
(min
utes
)
Morning Peak Midday Off-Peak Evening Peak
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Last Point -- Bus FrequencyLast Point -- Bus FrequencyLast Point -- Bus FrequencyLast Point -- Bus Frequency
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ConclusionsConclusions
Our attempt to fuse data sources appears promising Confirms TriMet buses can be probes Signal data needs work
Challenges Detailed AVL Data (Stop Level) Not Available in
Real Time (?) Travel Times Limited by Detector Data
What to “archive” is still an open question
Our attempt to fuse data sources appears promising Confirms TriMet buses can be probes Signal data needs work
Challenges Detailed AVL Data (Stop Level) Not Available in
Real Time (?) Travel Times Limited by Detector Data
What to “archive” is still an open question
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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
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Thank You!www.its.pdx.edu