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Robert L. Bertini Sirisha M. Kothuri Kristin A. Tufte Portland State University Soyoung Ahn Arizona...
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![Page 1: Robert L. Bertini Sirisha M. Kothuri Kristin A. Tufte Portland State University Soyoung Ahn Arizona State University 9th International IEEE Conference.](https://reader030.fdocuments.in/reader030/viewer/2022032612/56649f065503460f94c1c535/html5/thumbnails/1.jpg)
Robert L. BertiniSirisha M. Kothuri Kristin A. TuftePortland State University
Soyoung AhnArizona State University
9th International IEEE Conference on Intelligent Transportation Systems Toronto, CanadaSeptember 20, 2006
Development of an ITS Data Archive Application for Improving Freeway
Travel Time Estimation
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2
Outline
Introduction Study Area Data Sources Data Analysis Conclusions Next Steps
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Project Goals
1. Evaluation of Oregon Department of Transportation (ODOT) travel time estimating and reporting capabilities
2. Identify travel time algorithms for real time applications and historical analysis
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FHWA policy Variety of technologies
Inductive loop detectors Microwave radar Automatic vehicle tag matching Video detection License plate matching Cell phone matching
Past research General accuracy in free-flow conditions Recurring congestion & incidents more
challenging
Real-time Travel Time Estimates
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Portland ATMS Freeway Surveillance
485 inductive loop detectors (175 stations)
Dual loop (act as single loop) Mainline lanes Upstream of on-ramps
135 ramp meters 98 CCTV
ATIS www.TripCheck.com
Real-time speed map Static CCTV images
18 dynamic message signs (DMS) 3 display travel times
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15 directional freeway links I-5 (6) I-205 (3) I-84 (2) US-26 (2) OR-217 (2)
87 probe runs 516 miles driven by 12
drivers 15 hours of data collected
Travel Time Study Area
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PORTAL
National ITS Architecture ADUS
Funded by NSF Direct fiber-optic
connection between ODOT and PSU
20-second data Occupancy Volume Speed
Customized travel time area Conforms to TMOC
(Portland Regional Transportation Archive Listing)
www.portal.its.pdx.edu
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Ground Truth Data Hardware
Palm handheld computers
Magellan GPS devices Software
ITS-GPS Available at
www.its.pdx.edu Individual runs and groups
of probe vehicles Variety of traffic conditions
45 percent congested 2 notable incidents
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Travel Time – Midpoint Algorithm
Influence
Area 4Travel Time 4
(at t = 0)
Travel Time 1Influence
Area 1
Travel Time 3
(at t = 0)
Influence
Area 3
Travel Time 2
(at t = 0)
Influence
Area 2
Link Travel Time
(TT1 + TT2 + TT3 + TT4)
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Travel Time - Coifman Algorithm Coifman algorithm used upstream detector station speed
to estimate travel time.
c
j
jj
u
v
ht
1
hj = headway, vj = speed at detector, uc = speed of congested shock wave (assumed constant at 14 mph)
jjj tvx
tj and xj computed successively and added until the sum of all xj’s is greater than or equal to the link distance.
Ratio calculated so sum of distances Σxj is equal to link distance and travel time is multiplied by ratio to get link travel time.
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11
Travel Time - Coifman Algorithm
Time
Dis
tanc
e
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Analysis – Six Testing Scenarios
Coifman algorithm using speeds from upstream detectors only
Coifman algorithm using speeds from downstream detectors only
PP ttbttP VV , ,
PP ttattP VV , ,
2 while , , , d/ tVVV PttattbttP PPP
Coifman algorithm using speeds from both upstream and downstream detectors along with the midpoint influence areas 2 while , , , d/ tVVV PttattattP PPP
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Analysis – Testing Scenarios Contd..
Coifman algorithm using speeds from upstream and downstream detectors weighted in the ratio of distance of the hypothetical vehicle from each detector
Midpoint algorithm (based on influence areas)
Midpoint algorithm using speed at time (t = 0) that is an average of the upstream and downstream detector readings
d
tVVtVdVV PttattbPttatta
ttPPPPP
P
)]([)]([ , , , , ,
PPPP ttbttPttattP VVVV , , , , or
2 , ,
,PP
P
ttbttattP
VVV
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Analysis – Probe TT & TT Estimates
(a) Probe Vehicles
0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10 12 14 16 18 20
Probe Travel Time (min)
Es
tim
ate
d T
rave
l T
ime
(min
)
Coifman (u/s)
Coifman (d/s)
Midpoint
Coifman - Midpoint
Coifman - Distwt
Midpoint - Average
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14
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Analysis – Variance Comparisons
0
5
10
15
20
25
30
3 4 5 6 8 9 10 12 13
Link Number
Probe
Coifman (u/s)
Coifman (d/s)
Midpoint
Coifman - Midpoint
Coifman - Distwt
Midpoint - Average
Tra
vel
Tim
e (m
in)
+/-
On
e S
td D
ev
xxx
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Analysis – Free Flow Travel Times
292.00
293.00
294.00
295.00
296.00
297.00
298.00
299.00
300.00
17:03 17:05 17:07 17:09 17:11 17:13
Time
Mile
po
st (
mi.)
Probe
Coifman u/s
Coifman d/s
Midpoint
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Analysis – Incident Travel Times
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8:28 8:32 8:36 8:40 8:44 8:48 8:52 8:56 9:00
Time
Mil
ep
os
t (m
i.)
Probe
Coifman u/s
Coifman d/s
Midpoint
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Analysis – Large Detector Spacing
293.00
294.00
295.00
296.00
297.00
298.00
299.00
300.00
8:11 8:13 8:15 8:17 8:19 8:21 8:23
Time
Mil
ep
os
t (m
i.)
Probe
Coifman u/s
Coifman d/s
Midpt
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Analysis – Detector Spacing
Midpoint
R2 = 0.5077
R2 = 0.5915
0
20
40
60
80
100
120
140
0 0.5 1 1.5 2 2.5 3 3.5
Detector Spacing (mi.)
Tra
vel
Tim
e E
stim
atio
n E
rro
r (s
ec)
Uncongested
Congested
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Bus Probes
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Analysis - Bus TT & TT Estimates
0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10 12 14 16 18 20
(b) Bus
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14
Bus Travel Time (min)
Estim
ated
Tra
vel T
ime
(min
)Coifman (u/s)
Coifman (d/s)
Midpoint
Coifman - Midpoint
Coifman - Distwt
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Analysis – Variance Comparisons
0
5
10
15
20
25
30
5 6 7 12 13 14 19 20 21
Day
Bus
Coifman u/s
Coifman d/s
Midpoint
Coifman - Midpoint
Coifman - Distwt
Midpoint - Average
Tra
vel
Tim
e (m
in)
+/-
On
e S
td D
ev
xxx
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Conclusions
Travel times estimated by Coifman algorithm are more accurate than midpoint travel times.
The accuracy of travel times depends on Location and density of detectors Location, formation and dissipation of
queue Both algorithms misestimate when
incidents are encountered. Coifman algorithm more suited for
historical analysis in its current form.
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Next Steps
More probe data ITS data fidelity and its effect on
travel time estimates Assessment of performance of
algorithms with additional ground truth data
Sensitivity analysis Refinement of algorithms
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Acknowledgements
Dr. Chris Monsere Stacy Shetler Aaron Breakstone Dean Deeter Galen McGill ODOT TriMet Castle Rock Consultants PORTAL Team Peter Bosa Volunteer Drivers