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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
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
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
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
University of California
IrvineUniversity of California
IrvineUniversity of California
IrvineUniversity of California
Irvine
Study networkStudy network
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)
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
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
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
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%
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
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
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
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
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
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
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
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.
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
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
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
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)
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%)
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
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