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TOPL (Tools for Operations Planning):I-80 Freeway Corridor Monitoring and Control
Andy ChowGunes DervisogluGabriel GomesRoberto HorowitzAlex A KurzhanskiyJaimyoung KwonXiao-Yun LuAjith MuralidharanT Qi
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Tony QiuRene O. SanchezDongyan SuPravin Varaiya
090609
Outline
The AURORA architecture
Part I: System Overview
Part II: TOPL applied to the I-80 freeway
D t ll ti d d l lib ti
Part III: TOPL applied to the I-80/San Pablo corridor
Data collection and model calibrationImputating missing ramp flowsUsing Google Earth to refine the modelSimulating HOV lanesOnramp meteringPlan for VSL near the maze
Using GIS mapsModeling arterial traffic
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Modeling arterial traffic
Part IV: Future directionsTowards a Smart Corridor TMC
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Part I AURORA architectureAlex KurzhanskiyAlex Kurzhanskiy
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The TOPL system
manual inspection Scenarios
HOV lanesramp meteringVSLincidentsspecial events
Preliminary Aurora XML
The TOPL System
OpenJUMP filtering
GIS Importer
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
etc.
4
The TOPL System
network data, calibration & imputation
final model simulation & analysis
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Part II I-80 Data collection, model calibrationGunes Dervisoglou Gabriel GomesGunes Dervisoglou, Gabriel Gomes
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSLincidentsspecial events etc.
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Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
I-80 study section
I-80 Eastbound
23 miles
26 on-ramps and off-ramps26 on ramps and off ramps
loop detection at 55 locations
HOV lane with some HOV entrances/exits
I-80 Westbound23 miles29 on-ramps and 24 off-rampsloop detection at 57 locations
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loop detection at 57 locationsHOV lane with some HOV entrances/exits
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Model calibration
Step 1: Download daily PeMS detector health data for the study section, determine good days (73 good days in Jan – Sept 2008)
Step 2: TOPL Freeway Modeler generates freeway geometry andStep 2: TOPL Freeway Modeler generates freeway geometry and configuration along with a loop health ranking for the good days
Step 3: Choose calibration data from the good days
Step 4: TOPL Freeway Modeler automatically calibrates the model parameters and imputes missing ramp flows
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Step 5: TOPL Freeway Modeler generates configuration file for the AURORA simulation
Freeway Geometry (I-80 E)
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Link [int] Up Node [int]
Dn Node [int] Length [ft] Lanes [int] Auxlanes
[float] ML det. [vds] Has ML [bool]
1 1 2 792 6 0 401892 1
2 2 3 792 4 0 401892 0
3 3 4 1452 4 0 401657 1
4 4 5 1452 3 0 401657 0
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Select good days (I-80E)
Criteria:
– Detector Health: days with over 80% health during Jan-Sept 2008 C i S
EB
– Congestion Status
Moderate CongestionBoundaries in free-flow
– ‘Normal’ operating conditions
No special eventsHigh max. flow is indicator
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Calibrating fundamental diagram
Calibration: Process of fitting a fundamental diagram to density/flow data
For each vehicle detector station estimate:
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For each vehicle detector station estimate:
– Free flow speed
– Capacity
– Congestion wave speed
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Data collection and model calibration
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1. Free flow speed: Least square fit of data points with speed above 55 mph.
Data collection and model calibration
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1. Free flow speed: Least square fit of data points with speed above 55 mph.
2. Capacity : Maximum observed flow.
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Data collection and model calibration
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1. Free flow speed: Least square fit of data points with speed above 55 mph
2. Capacity : Maximum observed flow
3. Congestion line: 3.1. Divide congested data into “bins”3.2. Take maximum non-outlier point as representative of each bin3.3. Fit line pivoted at the maximum flow point
Assign parameters to all links
links without
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links withoutdetector
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Problems with the Berkeley Highway LabGabriel GomesGabriel Gomes
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSLincidentsspecial events etc.
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Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
Imputating missing ramp flowsAjith MuralidharanAjith Muralidharan
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSLincidentsspecial events etc.
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Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
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Imputation of missing ramp flows
Onramp and offramp flows are essential for simulation
But ramps often lack a functioning detector station
Motivation
But ramps often lack a functioning detector station
Use mainline measurements to infer ramp flows (as best we can)
What ramp flows will produce measured mainline flows in CTM?
Algorithm based on adaptive learning control theory
Approach
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Missing ramp flows
No ramp measurements are available
48 segments are lumped into 36 cells with working detectors
Impute onramp and offramp flows for ramps with missing/incorrect measurements
Simulate freeway operation using the imputed flows
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Simulated versus measured velocity contours
I-80E: Feb 19, 2009 (Th)
Simulated Speed0 70
PeMS Speed0 70
Tim
e [h
r]
5
10
15
20
20
30
40
50
60
Tim
e [h
r]
5
10
15
20
20
30
40
50
60
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PostMile10 15 20 25 30
20
0
10
PostMile10 15 20 25 30
20
0
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Simulated versus measured density, flow contours
Density Error = 4.18%e [h
r]
Simulated Density [veh/mile]0
5
10 200
250
300
e [h
r]
PeMS Density [veh/mile]0
5
10 200
250
300
PostMile
Tim
e
10 15 20 25 30
15
20 50
100
150
PostMile
Tim
e
10 15 20 25 30
15
20 50
100
150
Simulated Flow [veh/hr]0
9000
PeMS Flow [veh/hr]0
9000
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Flow Error = 7.57 %
PostMile
Tim
e [h
r]
10 15 20 25 30
5
10
15
20
0
1000
2000
3000
4000
5000
6000
7000
8000
PostMile
Tim
e [h
r]
10 15 20 25 30
5
10
15
20
0
1000
2000
3000
4000
5000
6000
7000
8000
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Simulated versus measured aggregate performance
VMT : 4.0% VHT : 1.5% Delay : 20%
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14 x 104
500
600
3000
3500Error is 1.6885
0 5 10 15 20 250
2
4
6
8
10
Time [hr]
VMT
[mile
s]
0 5 10 15 20 25
0
100
200
300
400
Time [hr]
Del
ay [h
r]
0 5 10 15 20 25
0
500
1000
1500
2000
2500
Time [hr]
VHT
[hr]
– PeMSSimulated
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– Simulated
Using Google Earth to refine networkDongyan SuDongyan Su
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSLincidentsspecial events etc.
23
Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
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On ramp from EB Cutting Blvd.
onramp E10 1. Confirm PeMS VDS and ramp information. Make note of missing ramps
Refining the model
On ramp from EB Cutting Blvd.
onramp E09
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E05Gilman St. onramp 1. Confirm PeMS VDS and
ramp information. Make note of missing ramps
Refining the model
2. Use Google Earth to gather onramp length and number of lanes.
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1. Confirm PeMS VDS and ramp information. Make note of missing ramps
Refining the model
3. Enter the information (missing ramps and onramp details) into the TOPL input spreadsheet
2. Use Google Earth to gather onramp length and number of lanes
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Simulating HOV lanesAjith Muralidharan Alex KurzhanskiyAjith Muralidharan, Alex Kurzhanskiy,
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSL incidentsspecial events etc.
27
Preliminary Aurora XML
The TOPL System
filtering
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
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HOV model in Aurora
Aurora permits
– Multiple vehicle types
– Multiple lanes as separate links
HOV models have
– Two vehicle types - HOV’s and SOV’s
– Freeway link consists of HOV and ML (general purpose) lanes
– Two modes of operation
HOV actuated- no SOVs in HOV lane
HOV not actuated– HOV lane operates as general purpose lane
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HOV not actuated HOV lane operates as general purpose lane
Split ratio Policy (I)
Flow exchange between lanes at nodes
Split ratios
– Specified separately for HOV’s and SOV’sp p y
– Unknown flow exchanges between HOV and SOV lane
Split ratio policy
– Indicate unknown split ratios in Aurora
– Specify partially filled split ratio matrices
Automatic policy to determine the split ratios
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Automatic policy to determine the split ratios
– Split ratios determined to maximize flows/ use downstream space
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Split ratio policy (II)
Indicate unknown split ratios
HOV HOV
Mainline Mainline
HOV vehicle split policy SOV vehicle split policy
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From \ To HOV Mainline Offramp
HOV * * 0.15
Mainline * * 0.15
Onramp * * 0
From \ To HOV Mainline Offramp
HOV 0 0.85 0.15
Mainline 0 0.85 0.15
Onramp 0 1 0
HOV lane actuated
I80E Model with HOV
HOV acutation hours: 5:00 AM – 10:00 AM and 3:00 PM -7:00 PM
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I80E Simulation: 1 in 5 vehicles is HOV capable
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I80E Simulation: 1 in 5 vehicles is HOV capable
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I80E Simulation: 1 in 4 vehicles is HOV capable
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I80E Simulation: 1 in 4 vehicles is HOV capable
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Larger delay on HOV, smaller delay on ML in 1 in 4 vs.1 in 5 scenario
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Onramp meteringGabriel GomesGabriel Gomes
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSL incidentsspecial events etc.
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Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
Aurora onramp metering algorithms
Aurora can be used to compare onramp metering algorithms
Algorithms currently implemented in Aurora:
– Time of day metering
– ALINEA
– SWARM 1/2a/2b (no dynamic bottlenecks yet)
TOD and Alinea are isolated controllers, SWARM is coordinated
Isolated controller Coordinated controller
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ALINEA ramp metering and queue override
Downstream mainline detectors
Onramp queue detector
Onramp merge detector
… Critical mainline density = capacity / freeflow speed… Alinea gain (we used 50 mph)
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Alinea:
Queue override: If onramp queue detector is occupied, set
No ramp metering
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ALINEA without queue override
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ALINEA with queue override
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Percent reduction in total travel time
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Case TTT [veh.hr] % reduction
No control 30,869 -
Alinea no override 30,452 1.35%
Alinea with override 30,538 1.07%
Percent reduction in delay
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Case Delay [veh.hr]
% reduction
No control 4,945 -
Alinea no override 2,513 49.2%
Alinea with override 3,292 33.4%
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Ramp queue as percent of ramp storage
200
250
300
350
400
onra
mp
stor
age
Case 1: No ramp control
200
250
300
350
400
onra
mp
stor
age
Case 2: Alinea without queue override
0 5 10 15 200
50
100
150
Per
cent
age
of
Time of day0 5 10 15 20
0
50
100
150
Per
cent
age
of
Time of day
350
400
Case 3: Alinea with queue override
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0 5 10 15 200
50
100
150
200
250
300
Per
cent
age
of o
nram
p st
orag
e
Time of day
queue override
Part II VSL control on I-80 W near MacArthur MazeXiao Yun Lu Tony QiuXiao-Yun Lu, Tony Qiu
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
HOV lanesramp meteringVSLincidentsspecial events etc.
46
Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
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Part III I-80/San Pablo corridor
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Using GIS mapsAndy Chow Jaimyoung KwonAndy Chow, Jaimyoung Kwon
HOV lanesramp meteringVSL incidentsspecial events etc.
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
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Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
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Construction of corridor network from GIS data
Test network: I-80/San Pablo corridor Starting point: Tele Atlas North America (TANA) GIS filesFour tools:
– OpenJUMP: Open source GIS tool Basic GIS manipulationOpenJUMP: Open source GIS tool. Basic GIS manipulation– TOPL GIS Importer. Export to AURORA format– AURORA Configurator: Manual adjustments
– AURORA Simulator: Run scenarios
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OpenJUMP
Initial cropping of GIS information
Joining Alameda and Contra Costa GIS files.
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GIS Importer
Retain desired street types
Retain desired street names (San Pablo, University, Solano, etc)
Simplify edges
Export to AURORA format
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GIS Importer for I-80/San Pablo corridor
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Filtered and simplified fileRaw GIS file
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GIS Importer for I-80/San Pablo corridor
Matlab-based experimental algorithm – only include I-80, San Pablo, and major streets /ramps connecting them
Parker Ave.Richmond PkwyHilltop Dr.El Portal Dr.
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San Pablo Dam RoadCuttingCentral Ave.BuchananGilmanUniversity Ave.Ashby Ave. Powell St.
Modeling arterial trafficAndy Chow Gabriel GomesAndy Chow, Gabriel Gomes
HOV lanesramp meteringVSL incidentsspecial events etc.
OpenJUMP filtering
GIS Importer
manual inspection Scenarios
54
Preliminary Aurora XML
The TOPL System
Aurora XML
Aurora Configurator
Aurora Simulator
Reports
Calibrationof model
parameters
Imputationof missing ramp flows
27
Arterial vs freeway traffic
Freeway Arterial
Maximum speed
65 mph 35 mphp
Capacity 2200 vphpl 1800 vphpl
Congestion Bottleneck induced Signal induced
Network Simple OD patterns with single routes.
Complex OD patterns with multiple routes.
Signals Single phase Up to 8 phases
Measurements Aggregate Aggregate + individual actuations
Al ith R l ti l i l R l ti l l (A t t d
calibration
OD estimation
signal design
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Algorithms Relatively simple (SWARM, Alinea)
Relatively complex (Actuated, adaptive, SCOOT, RHODES)
design
AURORA representation of a signalized intersection
• Links and nodes• Detector stations• Signals
Intersection nodeTraffic splitting nodeEntering traffic
• Signals
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Through trafficLeft-turn trafficExiting traffic
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AURORA arterial signal model
Each signal has 3 possible states: GREEN, YELLOW, and RED.
Signals affect traffic by adjusting the link capacity:
{GREEN, YELLOW} ⇒ Full capacity{GREEN, YELLOW} ⇒ Full capacity
{RED} ⇒ Zero capacity
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AURORA arterial detector station model
Each phase may have an approach and/or stopline detector station
Each detector station may contain one or more loop detectors
Each loop detector can measure
– Aggregate quantities (density, flow, speed)
– Individual vehicle calls
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AURORA signal control algorithms model
Signal control algorithms currently include
– Pre-timed (multiple plans and free operation)
– Isolated actuated
– Coordinated actuated
Signal control algorithms affect the signal state by issuing hold and force-off requests for particular phases
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AURORA model of arterial traffic
DEMO: A section of San Pablo Ave in Albany, CA
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AURORA model validation
Traffic data for May 23, 2008,1-1:30PM, from wireless sensors installed by Sensys Networks, Inc.
Fundamental diagram: g
Saturation flow = 1800 vph
Free-flow speed = 30mph
Critical density = 60 vpm
Jam density = 200 vpm
Current timing plan:
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Cycle time, phase durations, offsets, and phase sequences
Travel times for San Pablo network
300
350 Mean S.D. Matched 189.4 41.3Pretimed 189.7 18.4ASC 189.5 11.8
100
150
200
250
Trav
el ti
me (
sec)
Matched data Aurora - Pretimed
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0
50
46600 46800 47000 47200 47400 47600 47800 48000 48200 48400 48600
Entry time (sec)
Matched data Aurora Pretimed
Aurora - COORD HCM
*Note: Simulated network assumes 0 cross traffic
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Comparison with Paramics simulation
300
350
100
150
200
250
Trav
el tim
e (se
c)
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0
50
46600 47100 47600 48100 48600
Entry time (sec)
Aurora - Pretimed Paramics -pretimed
Part IV Future directions Alex Kurzhanskiy Pravin VaraiyaAlex Kurzhanskiy, Pravin Varaiya
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Towards a Smart Corridor TMC
securityproductivitynon-recur.i
recurrenti
alarm alarm alarm alarm
assessmentlosscongestioncongestion
controlcenter
scenariodatabase trusted, fast corridor simulator
Road network
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SCADAtraffic state estimation
and prediction
Supervisory Control And Data Acquisition
Real-time state estimation and short-term predictionflow
Model
Model prediction Measurement correction
density
200
300
400
ty (v
pm)
ActualLower boundUpper bound
Uncertainty Measurements
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0 50 100 150 200 250 300 350 4000
100
Time (minutes)
Den
sit
0 50 100 150 200 250 300 350 4000
20
40
60
80
Time (minutes)
Inte
rval
Siz
e (v
pm)
Just measurementsMeasurements + CTM
5 am 11 am