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![Page 1: Congestion Management Innovations in Oregon Christopher Monsere Assistant Professor Portland State University Civil and Environmental Engineering Director,](https://reader035.fdocuments.net/reader035/viewer/2022070406/56649e055503460f94af1ce9/html5/thumbnails/1.jpg)
Congestion ManagementInnovations in
OregonChristopher Monsere
Assistant ProfessorPortland State University
Civil and Environmental EngineeringDirector, Intelligent Transportation Systems
Laboratory
ITS
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Outline
• Portland, Oregon Regional Approach• Freeway Performance• Arterial Performance• Environmental Performance
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Portland, Oregon - USA
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Portland, Oregon - USA
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Portland, Oregon - USA
Population 2.2 million
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A Regional Approach
• TransPort ITS Coordinating Committee
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PORTAL -- The Portland Region’s Archived Data User Service (ADUS)
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What’s in the PORTAL Database?
Loop Detector Data20 s count, lane occupancy, speed from 500 detectors (1.2 mi spacing)
Incident Data140,000 since 1999
Weather DataEvery day since 2004
VMS Data19 VMS since 1999
DaysSince July 2004About +700 GB6.9 Million Detector Intervals
Bus Data1 year stop level data140,000,000 rows
001590
WIM Data22 stations since 200530,026,606 trucks
Crash DataAll state-reported crashes since 1999 - ~580,000
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Freeway Performance
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Performance Measures Used
• Volume• Speed• Occupancy• Vehicle Miles Traveled• Vehicle Hours Traveled• Travel Time• Delay• Reliability
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Interstate 5 Northbound
About 38.6 kilometers
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Estimated Monthly Travel Time I-5 North September 2006
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
60.0
65.0
70.0
0:00
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:0
0
11:0
0
12:0
0
13:0
0
14:0
0
15:0
0
16:0
0
17:0
0
18:0
0
19:0
0
20:0
0
21:0
0
22:0
0
23:0
0
Time
Trav
el T
ime
(min
)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Perc
ent C
onge
sted
Percent Congested
Free Flow Travel Time
Mean Travel Time
95th Percentile Travel Time
Lyman and Bertini, 2007
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Travel Time Comparison, Northbound I-5, September 2004-2006
22.0
24.0
26.0
28.0
30.0
32.0
34.0
36.0
38.0
40.0
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Time
Trav
el T
ime
(min
)
20062005
2004
From monthly performance reports
Lyman and Bertini, 2007
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Systematically Identifying Bottlenecks
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Systematically Identifying Bottlenecks
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Systematically Identifying Bottlenecks
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Arterial Performance
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Objective
Develop an automated way to report SpeedsTravel timesPerformance measures
Using Existing ITS signal infrastructureAutomatic Vehicle Locator (AVL) data
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Speed Map Generated from TriMet Bus AVL System Data Only
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ITS
Midpoint Method Using 5-Minute DataSi
gnal
ized
Inte
rsec
tions
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ITS
Adjust Influence Areas ManuallySi
gnal
ized
Inte
rsec
tions
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ITS
Bus Data Confirms AdjustmentSi
gnal
ized
Inte
rsec
tions
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ITS
Reveals Gaps in DetectionSi
gnal
ized
Inte
rsec
tions
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ITS
New Occupancy Map From Combined Sources
Sign
aliz
ed In
ters
ectio
ns
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ITS
An Improvement Over Mid-Point MethodSi
gnal
ized
Inte
rsec
tions
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ITS
Obstacles• System Signal Detector– Very Limited Aggregation– Access to Real Time Data– Limited Detection & Spacing
• Bus– Access to Real Time Data
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ITS
Next 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
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Glossary• MAC Address: a 48 bit (>28
trillion) unique address assigned to a device by its manufacturer.
• Bluetooth: a wireless protocol utilizing short-range communications technology facilitating data transmission over short distances from fixed and/or mobile devices
ClassMaximum
PowerOperating Range
Class 1100mW (20dBm)
100 meters
Class 22.5mW (4dBm)
10 meters
Class 31mW
(0dBm)1 meter
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Estimated Travel Time Example
AddressFirst-First Travel
TimeLast-Last Travel
Time00:10:86:e8:56:14 0:05:00 0:05:0000:1e:45:69:4d:1f 0:05:12 0:05:1200:c0:1b:04:d6:9d 0:06:06 0:05:2500:15:b9:d2:82:e2 0:05:55 0:05:55
Not always a trivial distinction…some thought
needs to be given to geometrics/physics
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Powell Blvd Corridor Bluetooth reader locations
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-15
-10
-5
0
5
10
15
12:00 PM 12:15 PM 12:30 PM 12:45 PM 1:00 PM 1:15 PM 1:30 PM
1003 (33rd)1002(47th)1004 (53rd)
Travel Times(13th <-> 53rd )
East
boun
d TT
(Min
)W
est b
ound
TT
(Min
)
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Environmental Performance
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Arterial Fusion Project
• Create framework to fuse – Bus Probe Data– Matched Vehicle Probe Data– Adaptive Signal System Data– Private Sector Data?
• In to one complete picture
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Sustainability Performance Measures Using Archived ITS Data:
1. Emissions Estimates2. Fuel Consumption3. Cost of Delay4. Person Mobility (PMT, PHT, PHD)
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Emissions Measure Methodology
MOBILE inputs generated from PORTAL and gathered local data
MOBILE model run for locations and time periods of interest
MOBILE output database processed to establish emissions rates
Emissions rates combined with PORTAL travel data (VMT) to determine freeway segment emissions
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Hourly CO2 Estimate
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230
1,000
2,000
3,000
4,000
5,000
6,000
7,000Hourly CO2 on I-5 NB at
Broadway
Hour of Day, July 1, 2005
Pou
nd
s o
f C
arb
on
Dio
xid
e
I-5 M
P 30
2.5
(1.4
mile
sec
tion)
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CO Emissions From Congestion
I-5 M
P 30
2.5
(1.4
mile
sec
tion)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230
20
40
60
80
100
120Hourly CO Emissions and Con-
gestion60mph Free Flow
Actual Speeds
Hour of Day, July 1, 2005
Pou
nd
s o
f C
arb
on
Mon
oxid
e
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Acknowledgments• R.L. Bertini - ITS Lab and PORTAL founder• Colleagues –
• Kristin Tufte, Miguel Figliozzi, Ashley Haire, Portland State University• Peter Koonce, Shaun Quayle Kittelson and Associates• Darcy Bullock, Purdue University• Willie Rotich and Paul Zabell, Portland Bureau of Transportation
• Sponsors -• National Science Foundation• Oregon Department of Transportation• Federal Highway Administration • TransPort ITS Coordinating Committee• City of Portland, Office of Transportation• TriMet• Oregon Engineering and Technology Industry Council
• Students
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ReferencesMAC Address Tracking• Wasson, J.S., J.R. Sturdevant, D.M. Bullock, “Real-Time Travel Time Estimates Using MAC Address Matching,”
Institute of Transportation Engineers Journal, ITE, Vol. 78, No. 6, pp. 20-23, June 2008.• Bullock, D.M., C.M. Day; J.S. Sturdevant, ”Signalized Intersection Wasson J.S., S.E. Young, J.R. Sturdevant, P.J.
Tarnoff, J.M. Ernst, and D.M. Bullock, , “Evaluation of Special Event Traffic Management: The Brickyard 400 Case Study,” under review.
Cycle by cycle and Movement based Performance Measures• Performance Measures for Operations Decision Making,” Institute of Transportation Engineers Journal, ITE,
Vol. 78, No. 8, pp. 20-23, August 2008.• Hubbard, S.M.L., D.M. Bullock, and C. Day “Opportunities to Leverage Existing Infrastructure To Integrate
Real-Time Pedestrian Performance Measures Into Traffic Signal System Infrastructure,” Paper ID: 08-1392, submitted July 2007, revised October 2007, in press.
• Day, C., E. Smaglik, D.M. Bullock, and J. Sturdevant, ”Quantitative Evaluation of Actuated Versus Nonactuated Coordinated Phases,” Paper ID: 08-0383, submitted July 2007, revised October 2007, in press.
• Smaglik E.J., A. Sharma, D.M. Bullock, J.R. Sturdevant, and G. Duncan, “Event-Based Data Collection for Generating Actuated Controller Performance Measures," Transportation Research Record, #2035, TRB, National Research Council, Washington, DC, pp.97-106, 2007.
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ITS
Thank You!
www.its.pdx.edu
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Extra slides – no translation past
this slide
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MOBILE 6.2
1. New facility-specific drive cycles recorded in modern American cities
2. Updated vehicles, emissions rates, regulatory programs, and driver behaviors
3. Fuel consumption and CO2 estimates not speed-dependent (only based on fuel and fleet data)
4. Non-specified parameters default to national averages (many county-specific data available from the EPA)
Improvements and caveats
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Average Speed Emissions Models• Model Development Process:
Record Drive Cycles
• Probe vehicles on complete trips
• Representative set of conditions
• Key to accuracy of model
Test Vehicles
• Run vehicles through drive cycles on a dynamometer
• Representative set of vehicles from roadway fleet
• Important to capture range of conditions, size, age, etc.
Avg. Speed Emission Rates
• Link emissions to vehicle classes at average drive cycle speeds
• Facility-specific drive cycles can capture congestion effects
Calculate Emissions with rates and travel
• Uses VMT and emissions rates
• Emissions rates can be modified by other inputs (weather, fuel programs, etc.)