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ITS Lab Members
1
Welcome, Dr. Levinson!
PSU ITS Lab: Bertini Group
ITS Lab Members
3rd year Ph.D. Student in Computer Science
Areas of Interest Data Stream Management Systems
Intelligent Transportation Systems
Thesis Topic: Inter-operator feedback, bounded execution guarantees
2Rafael J. Fernández-Moctezuma
Rafael J. Fernández-Moctezuma
ITS Lab Members
• Deal efficiently with high-volumes of incoming data
• Traffic Data CS theory– Inter-Operator Feedback– Guarantees on Bounded Stream
Query Execution
Work with Prof. Maier, and Prof. Tufte
3Rafael J. Fernández-Moctezuma
Data Stream Management Systems
(a) Centralized Adaptation (b) Localized Adaptation
DUPLICATE
σC σ¬C
IMPUTE
PACE
ITS Lab Members
• Big picture: Adapt to incoming data characteristics to perform near real-time imputation
• Looked at diverse strategies, not all amicable for low latency processing
• Spatial and Temporal models, some heuristic, some statistical.
Work with Prof. Bertini, Prof. Maier, and Prof. Tufte
4Rafael J. Fernández-Moctezuma
On-Line Imputation Strategies
A B C
SB SCSA
Direction of flow
ITS Lab Members
• Work toward automatic bottleneck detection
• “Living history” of Portland Bottlenecks
• Can process one year of data per corridor in one day (commodity PC)
Work with Prof. Bertini, Jerzy Wieczorek, Huan Li
Bottleneck Identification
5Rafael J. Fernández-Moctezuma
ITS Lab Members
• Where do we position loop detectors to better operate the freeway infrastructure?
• Challenges: What’s “better”? Optimal ramp metering? Better travel time estimations? Early bottleneck detection?
• Recently focused on Linear Programming approach for early bottleneck detection
Work with Prof. Figliozzi, Prof. Bertini
6Rafael J. Fernández-Moctezuma
Optimal Sensor Placement
ITS Lab Members
1st year Graduate Student in Transportation Engineering
Current Research Topics in the ITS Lab Impacts of Sensor Spacing on Accurate Freeway Travel
Time Estimation for Traveler Information
Dynamic Bi-level Programming Models for Distribution Centers Location
7Wei Feng
Wei Feng
ITS Lab Members
• Compute VHT errors of different travel time estimation methods where transition happens
8Wei Feng Work with Porf. Bertini
Travel Time Estimation/Sensor Spacing
• Calculate relationship between all types of errors and sensor spacing for each method
ITS Lab Members
• Minimize the combined cost of VHT error cost and sensor construction cost.
• Express optimal sensor spacing with parameters: speed, flow and cost coefficients.
• Sensitivity analysis of parameters to the optimal sensor spacing.
9Wei Feng Work with Porf. Bertini
Travel Time Estimation/Sensor Spacing
ITS Lab Members
Travel Time Estimation/Sensor Spacing
10Wei Feng Work with Porf. Bertini
• Convert absolute VHT error or percentage VHT error into money, and how to set the conversion coefficient?
• What would be the reasonable constraints of absolute VHT error and percentage VHT error when applying optimization method?
ITS Lab Members
• Upper level: Minimize total cost (system minimization)
• Lower level: Minimize distribution cost (customer minimization)– Radial distribution– Multi TSP distribution– Multi VRP distribution
11Wei Feng Work with Porf. Figliozzi
Dynamic Bi-level Model for DC Location
• Solution Algorithm: Cluster and Approximation
ITS Lab Members
Ph.D Student in Civil Engineering
Areas of Interest Freeway Management and Operation
Transit Operation
Intelligent Transportation System
Climate Change
12Huan Li
Huan Li
ITS Lab Members
• Assess optimal stop spacing considering access cost and riding & stopping cost
13Huan Li
Transit Service Evaluation
On-BoardComputer
Radio
DoorsLift
APC (Automatic Passenger Counter)
Overhead SignsOdometer
Signal Priority EmittersStop Annunciation Memory Card
RadioSystem
Garage PC’s
Radio AntennaGPS Antenna
Navstar GPS Satellites
Control Head
• Use high resolution archived stop-level data– One year’s worth of data– Referring all routes in
Portland Metropolitan region every trip every bus stop event
ITS Lab Members
14Huan Li
Transit Service EvaluationR
ou
te N
o.
Se
rvic
eD
ate
Le
av
e T
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Sto
p T
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Arr
ive
Tim
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dg
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Dir
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tio
n
Tri
p N
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Lo
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Dw
ell
Do
or
Lif
t
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pe
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tte
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14 01NOV2001 8:53:32 8:49:15 8:53:28 285 0 1120 4964 0 0 0 0 0 21 41 10558.58 7644468 67600514 01NOV2001 8:55:00 8:51:41 8:54:46 285 0 1120 4701 4 0 0 0 1 20 50 15215.05 7649112 67632814 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
ITS Lab Members
• Analyze lane changing effect on speed using lane by lane oblique curve
• “Historical data”• Automatically identify HOV lane
merging and diverging features– Indicator: piece wised linear regression for
curve fitting– Endogenous Model vs. Extraneous Model
Traffic Flow Features on HOV lane
15Huan Li
ITS Lab Members
• Next step: compose oblique method with threshold based identification method
• Other applications: incident detection, bottleneck identification….
16Huan Li
Traffic Flow Features on HOV lane
ITS Lab Members
• Civil engineering undergraduate, senior (focus on transportation)
• Areas of Interest– Transp. system sustainability
– Modeling transp. emissions and diffusion
• Honor Program – Thesis topic: Carbon Sponsoring for Personal Travel
17Alex Bigazzi
Alex Bigazzi
ITS Lab Members
• Sustainability performance measures for the transportation data archive at PSU– Emissions: currently MOBILE 6.2, will use MOVES– Fuel Consumption– Cost of Delay– Personal Mobility (PHT, PHD, PMT)
18Alex Bigazzi
‘Greening’ PORTAL
ITS Lab Members
• Errors from temporal aggregation
• Data source: disaggregate speeds from loop data
• Event: car passes over loop• Error 1: Time resolution
– Shock speed
19Alex Bigazzi
ITS Data Aggregation Effects
10 20 30 60 120 300 6000%
10%
20%
30%
40%
50%Shock Speed Error
0.5 km
2.0 km
Aggregation Width (sec)
MA
PE
ITS Lab Members
• Error 2: Parameter distribution– Speed distribution narrows
• Underestimate emissions, delay
– Travel time errors from using time mean speed
• Underestimate delay• Corrected using harmonic mean• Can be estimated w/ variance
ITS Data Aggregation Effects
20Alex Bigazzi
10 20 30 60 120 300 600 900 1800 3600
Aggregated Delay, Calulated and Lost
Aggregation Width (sec)
Dela
y (1
000 h
ours
)
0
5
10
15
20
Aggregate EstimateDistribution ErrorTMS Error
ITS Lab Members
• Framework for individuals to seek direct, voluntary carbon offsets for personal travel
• Targets carbon reductions outside of current monetary-based offset programs
• Project objectives:– Establish calculation methods for carbon outputs– Develop effective and simple online interface– Analyze initial feedback from pilot users
21Alex Bigazzi
CarbonSponsor.org
ITS Lab Members
1st year M.S. Student in Civil Engineering
Areas of Interest Traffic Flow Theory
Intelligent Transportation Systems
Possible Thesis Topics: Uncertainty Propagation in Traffic Flow Models or Bottleneck Identification using FOTO and ASDA Models
22Meead Saberi K.
Meead Saberi K.
ITS Lab Members
• Dealing with two large databases of traffic data and weather data (PORTAL)
• Traffic and weather data fusion and quality
23Meead Saberi K.
Effects of Weather on Traffic Flow on Freeways
ITS Lab Members
• Effects of precipitation, visibility and wind speed on: speed and flow (average, standard deviation, and statistical significance)
• Probabilistic Approach: using cumulative distribution function
24Meead Saberi K.
Effects of Weather on Traffic Flow on Freeways
35
40
45
50
55
60
6 7 8 9 10 11
Time
Sp
eed
No Rain Very Light Rain Light Rain Moderate Rain
0
5
10
15
20
25
30
35
40
45
50
10andless
10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60and
more
Speed (mph)
Fre
qu
en
cy (
%)
No Rain
Rainy
0
10
20
30
40
50
60
70
80
90
100
10 15 20 25 30 35 40 45 50 55 60
Average Speed (mph)
1 -
Cu
mu
luati
ve D
istr
ibu
tio
n F
un
cti
on
ITS Lab Members
25Meead Saberi K.
Segment Level Analysis of Travel Time Reliability
1
2
3
4
5
6
7
8
0:00 6:00 12:00 18:00
Time of Day
Tra
vel
Tim
e C
ost
($)
Average Travel Time Standard Deviation of Travel Time
Breaking the overall I-5 NB freeway into shorter segments; this study shows how travel time reliability can vary across freeway segments using different reliability measures.
ITS Lab Members
26Meead Saberi K.
Segment Level Analysis of Travel Time Reliability
0:002:15 4:30
6:45 9:0011:15
13:3015:45
18:0020:15
22:30286.1
289.4
290.54
293.18
295.18
296.6
299.7
302.5
304.4
306.51
307.9
0.00
0.25
0.50
0.75
1.00
1.25
1.50
0
0.2
0.4
0.6
0.8
1
1.2
1.4
28
6.1
28
6.3
28
9.4
28
9.6
29
0.5
29
2.2
29
3.2
29
3.7
29
5.2
29
6.3
29
6.6
29
7.3
29
9.7
30
1.1
30
2.5
30
3.9
30
4.4
30
5.1
30
6.5
30
7.5
30
7.9
S e g me nt
Bu
ffe
r In
de
x
n
i jijii SSSVarCVar
1
),cov(2)()(
• Segment ranking based on travel time reliability
• Reliability of corridor vs. segments
ITS Lab Members
Master Student in Civil engineering at the ENTPE
5 month internship at the ITS Lab
Areas of Interest: Traffic flow theory
Transportation Economics
27Helene Siri
Helene Siri
ITS Lab Members
ENTPE : Civil engineering school (Lyon) Structure, environment, urbanism and transportation
Transportation department at the ENTPE LET (CNRS - University of Lyon II – ENTPE)
LICIT (INRETS – ENTPE)
28Helene Siri
About the ENTPE
ITS Lab Members
Loop Detector Data from lane 3 northbound station 20 on the I-880 freeway
Aggregation of data indifferent samplingperiods
29Helene Siri
A practice study for ITS data aggregation
5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 100
500
1000
1500
2000
2500
3000
3500
30 sec1min5min15min
Time (h)Flo
w (
veh/h
)
ITS Lab Members
Net speed on a urban grid with different densities of intersections and different legal posted speed
Developing a program using Matlab to estimate Net speed
30Helene Siri
Net Speed Calculator
ITS Lab Members
ITS Data Aggregation using NGSIM Data
31Helene Siri
Next step:
ITS Lab Members
2nd year M.S. Student in Statistics
ITS Research: Historical and real-time bottleneck identification
Statistics Research: Minimum Kolmogorov-Smirnov Estimation (MSKE) with
censored data
32Jerzy Wieczorek
Jerzy Wieczorek
ITS Lab Members
• Using speed data to track historical congestion and rank bottlenecks by cost
• Incorporating historical information into model to predict real-time bottleneck behavior
Work with Prof. Bertini, Huan Li,Rafael J. Fernández-Moctezuma
Bottleneck Identification
33Jerzy Wieczorek
A
B
C
Bottleneck
Estimated Propagation Speed
A – 25 mphB – 22 mphC – 21 mph
Activation
Deactivation
90% percentile of historicalbottlenecks
A
B
C
Bottleneck
Estimated Propagation Speed
A – 25 mphB – 22 mphC – 21 mph
Activation
Deactivation
90% percentile of historicalbottlenecks
A
B
C
Bottleneck
Estimated Propagation Speed
A – 25 mphB – 22 mphC – 21 mph
Activation
Deactivation
90% percentile of historicalbottlenecks
ITS Lab Members
• Expanding model to use volume data
• Validating against ground truth from Bertini-Cassidy method
Work with Prof. Bertini, Huan Li,Rafael J. Fernández-Moctezuma
Bottleneck Identification
34Jerzy Wieczorek
0 20 40 60 80 1000
500
1000
1500
2000
2500
3000
NB I-5 Detector 12 (Milepost 299.7), Feb 7 2008
Speed (mph)
Vol
ume
(vpl
ph)
FreeflowCongested
ITS Lab Members
• Choose distribution and parameter estimates that minimize the K-S statistic (max. vertical difference in CDFs)
Work with Prof. Kim35Jerzy Wieczorek
Minimum K-S Estimation
θ = 96.1
θ = 72.2
ITS Lab Members
• Extend to censored data
• Evaluate in comparison with MLEs (standard)
• Create R library if worthwhile
Work with Prof. Kim
36Jerzy Wieczorek
Minimum K-S Estimation
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