a calibration procedure for microscopic traffic simulation lianyu chu, university of california,...
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![Page 1: A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western](https://reader031.vdocuments.site/reader031/viewer/2022032801/56649ddd5503460f94ad63f9/html5/thumbnails/1.jpg)
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
A Calibration Procedure for Microscopic A Calibration Procedure for Microscopic Traffic Simulation Traffic Simulation
Lianyu Chu, University of California, Irvine
Henry Liu, Utah State University
Jun-Seok Oh, Western Michigan University
Will Recker, University of California, Irvine
![Page 2: A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western](https://reader031.vdocuments.site/reader031/viewer/2022032801/56649ddd5503460f94ad63f9/html5/thumbnails/2.jpg)
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
OutlineOutline
• Introduction
• Data preparation
• Calibration
• Evaluation of the overall model
• Discussion
• Conclusion
![Page 3: A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western](https://reader031.vdocuments.site/reader031/viewer/2022032801/56649ddd5503460f94ad63f9/html5/thumbnails/3.jpg)
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Introduction to Introduction to Microscopic simulationMicroscopic simulation
• Micro-simulation models / simulators– AIMSUN, CORSIM, MITSIM, PARAMICS, VISSIM…– model traffic system in fine details
• Models inside a simulator– physical components
– roadway network, traffic control systems, driver-vehicle units, etc
– associated behavioral models– driving behavior models, route choice models
• To build a micro-simulation model:– complex data requirements and numerous model parameters– based on data input guidelines and default model parameters
![Page 4: A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western](https://reader031.vdocuments.site/reader031/viewer/2022032801/56649ddd5503460f94ad63f9/html5/thumbnails/4.jpg)
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
ObjectiveObjective
• Specific network, specific applications
• Calibration:– adjusting model parameters
– until getting reasonable correspondence between model and observed data
– trial-and-error, gradient approach and GA
• Current calibration efforts: incomplete process– driving behavior models, linear freeway network
• Objective: – a practical, systematic procedure to calibrate a
network-level simulation model
![Page 5: A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western](https://reader031.vdocuments.site/reader031/viewer/2022032801/56649ddd5503460f94ad63f9/html5/thumbnails/5.jpg)
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Study networkStudy network
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Data inputsData inputs
• Simulator: Paramics• Basic data
– network geometry – Driver Vehicle Unit (DVU)
– driver behavior (aggressiveness and awareness factors)– Vehicle performance and characteristics data
– vehicle mix by type– traffic detection / control systems– transportation analysis zones (from OCTAM)– travel demands, etc.
• Data for model calibration – arterial traffic volume data – travel time data– freeway traffic data (mainline, on and off ramps)
![Page 7: A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western](https://reader031.vdocuments.site/reader031/viewer/2022032801/56649ddd5503460f94ad63f9/html5/thumbnails/7.jpg)
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Freeway traffic data reductionFreeway traffic data reduction
• Why– too many freeway data, showing real-world traffic variations– calibrated model should reflect the typical traffic condition
of the target network– find a typical day, use its loop data
• How to find a typical day– vol(i): traffic volume of peak hour (7-8 AM)– ave_vol: average of volumes of peak hour – investigating 35 selected loop stations
– 85% of GEH at 35 loop stations > 5
2/)_)((
_)( 2
VolaveiVol
VolaveiVolGEH
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Calibration procedureCalibration procedure
N
Y
Calibration of driving behavior models
Total OD estimation
Route choice adjustment
Reconstruction of time-dependent OD demands
Model Fine-tuning
Volume, Traveltime match?
Overall modelvalidation / evaluation
Basic data input / Network coding
Calibration of routing behavior model
Reference OD from planning model
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Determining number of runsDetermining number of runs
N
Y
Original nine runs
Start
Calculating the mean and its std of each performance measure
Is current # of runs enough?
End
Calculating the required # of runs for each performance measure
Additional one simulation run
22/ )(
tN
• μ, δ: – mean and std of
MOE based on the already conducted simulation runs
• ε: allowable error• 1-α: confidence
interval
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Step 1/2: Calibration of Step 1/2: Calibration of driving behavior / route behavior models driving behavior / route behavior models
• Calibration of driving behavior models:– car-following (or acceleration) , and lane-changing
– sub-network level
– based on previous studies– mean target headway: 0.7-1.0– driver reaction time: 0.6-1.0
• Calibration of route behavior model – on a network-wide level. – using either aggregated data or individual data– stochastic route choice model
– perturbation: 5%, familiarity: 95%
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Step 3: OD EstimationStep 3: OD Estimation
• Objective: time-dependent OD
• Method:– first, static OD estimation– then, dynamic OD
• Procedure:– Reference OD matrix– Modifying and balancing the reference OD demand– Estimation of the total OD matrix – Reconstruction of time-dependent OD demands
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Reference OD matrixReference OD matrix
• Reference OD matrix– from the planning model, OCTAM
• Modifying and balancing the reference OD demand– problems with the OD from planning model
– limited to the nearest decennial census year– sub-extracted OD matrix based on four-step model– morning peak hours from 6 to 9; congestion is not cleared at 9 AM
– balancing the OD table: FURNESS technique – 15-minute counts at cordon points (inbound and outbound)– total generations as the total
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Estimation of the total OD matrixEstimation of the total OD matrix
• A static OD estimation problem– least square– tools, e.g. TransCAD, QueensOD, Estimator of Paramcis
• Our method:– simulation loading the adjusted OD matrix evenly– 52 measurement locations (13 mainline, 29 ramp, 10 arterial)– quality of estimation: GEH
– GEH at 85% of measurement locations < 5
– modification of route choices– OD adjustment algorithm: proportional assignment
– assuming the link volumes are proportional to the OD flows
• Result: – 96% of all measurement locations < 5
2/))()((
)()( 2
nMnM
nMnMGEH
simobs
simobs
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Reconstruction of time-dependent OD Reconstruction of time-dependent OD
• A dynamic OD demand estimation problem – research level, no effective method
– a fictitious network or a simple network
– practical method:– FREQ: freeway network– QueensOD, Estimator of PARAMICS, etc.
• Profile-based method:– profile: temporal traffic demand pattern – based on the total OD demand matrix– assign total OD to a series of consecutive time slices
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Finding OD profilesFinding OD profiles
• Find the profile of each OD pair• General case (from local to local):
– profile(i, j) = profile(i) , for any origin zone, j =1 to N, – profile(I): vehicle generation pattern from an origin zone
• Special cases: – local to freeway
– estimated by traffic count profile at a corresponding on-ramp location
– freeway to local– estimated by traffic count profile at a corresponding off-ramp location
– freeway to freeway*– roughly estimated by traffic count profile at a loop station placed on
upstream of freeway mainline – needs to be fine-tuned
• volume constraint at each time slice
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Examples of OD profilesExamples of OD profiles
Destination Origin 1 2 3 4
profile(i) (known)
1
2
3
4
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Fine-tuning OD profilesFine-tuning OD profiles
• Optimization objectives– Min (Generalized Least Square of traffic counts
between observed and simulated counts over all points and time slices)
– step 1:minimizing deviation of peak hour (7-8 AM)– criteria: more than 85% of the GEH values < 5
– step 2: minimizing deviation of whole study period at five-minute interval
– together with next step
– 52 measurement points
• Result: – step 1: 87.5% of all measurement locations
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Step 4: overall model fine-tuningStep 4: overall model fine-tuning
• Objectives: – check/match local characteristics: capacity, volume-
occupancy curve– further validate driving behavior models locally– reflect network-level congestion effects
• Calibration can start from this step if:– network has been coded and roughly calibrated.– driving behavior models have been roughly calibrated and
validated based on previous studies on the same network. – one of the route choice models in the simulator can be
accepted.– OD demand matrices have been given.
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Model fine-tuning methodModel fine-tuning method
• Parameters:– Link specific parameters
– signposting setting– target headway of links, etc
– Parameters for car-following and lane-changing models– mean target headway – driver reaction time
– Demand profiles from freeway to freeway
• Objective functions: – min (observed travel time, simulated travel time)– min (Generalized Least Square of traffic counts over all
points and periods)
• Trial-and-error method
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Some calibrated OD profilesSome calibrated OD profiles
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
6:00 6:15 6:30 6:45 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 9:15 9:30 9:45
Time of day
Per
cen
tag
e o
f to
tal
dem
and
a freeway zone to a freeway zone an arterial zone to an industrial zone
a freeway zone to an arterial zone an artertial zone to a freeway zone
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
volume-occupancy curve volume-occupancy curve
0
2040
60
80100
120
0 20 40 60 80
Percent occupancy
30-s
ec
Vo
lum
e
0
20
4060
80
100
120
0 20 40 60 80
Percent occupancy30
-se
c V
olu
me
Real world Simulation
Loop station @ 2.99
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Evaluation of CalibrationEvaluation of Calibration (I) (I)
• Measure for goodness of fit: – Mean Abstract Percentage Error (MAPE)
T
tobssimobs tMtMtM
TMAPE
1
))(/))()(((1
Comparison of observed and simulated travel time of SB / NB I-405
0
100
200
300
6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00
Trav
el t
ime
(sec
)
simulation observation
0
200
400
600
6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00
Tra
ve
l tim
e (
se
c)
simulation observation
3.1% (SB) 8.5% (NB)
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Evaluation of CalibrationEvaluation of Calibration (II) (II)
0
100
200
300
400
500
600
700
6:05 6:30 6:55 7:20 7:45 8:10 8:35 9:00 9:25 9:50
405N0.93ml-sim 405N0.93ml-real
0
50
100
150
200
250
6:05 6:30 6:55 7:20 7:45 8:10 8:35 9:00 9:25 9:50
405N1.93ff-sim 405N1.93ff-real
0
200
400
600
800
1000
405N3.04ml-sim 405N3.04ml-real
0
200
400
600
800
1000
405N3.86ml-sim 405N3.86ml-real
0
200
400
600
800
1000
405S3.31ml-sim 405S3.31ml-real
0
50
100
150
200
133s9.37ml-sim 133s9.37ml-real
5-min traffic count calibration at major freeway measurement locations(Mean Abstract Percentage Error: 5.8% to 8.7%)
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
DiscussionDiscussion
• Completeness and quality of the observed data– Especially important for calibration result– Quality of the observed data
– Calibration errors might have been derived from problems in observed data
– Probe vehicle data with about 15-20 minute intervals cannot provide a good variation of the travel time
– Quantity / Availability of observed data – cover every part of the network – some parts of the network were still un-calibrated
because of unavailability of data
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University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
ConclusionConclusion
• Conclusion– a calibration procedure for a network-level simulation model
– responding to the extended use of microscopic simulation
– the calibrated model:– reasonably replicates the observed traffic flow condition
– potentially applied to other micro-simulators
• Future work:– inter-relationship between route choice and OD estimation – an automated and systematic tool for microscopic
simulation model calibration/validation