A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California,...

University 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

Irvine

OutlineOutline

• Introduction

• Data preparation

• Calibration

• Evaluation of the overall model

• Discussion

• Conclusion

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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

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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

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Study networkStudy network

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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)

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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/)_)((

VolaveiVol

VolaveiVolGEH

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Calibration procedureCalibration procedure

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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Determining number of runsDetermining number of runs

Original nine runs

Calculating the mean and its std of each performance measure

Is current # of runs enough?

Calculating the required # of runs for each performance measure

Additional one simulation run

22/ )(

• 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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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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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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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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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

nMnMGEH

simobs

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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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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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Examples of OD profilesExamples of OD profiles

Destination Origin 1 2 3 4

profile(i) (known)

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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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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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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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Some calibrated OD profilesSome calibrated OD profiles

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

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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volume-occupancy curve volume-occupancy curve

0 20 40 60 80

Percent occupancy

0 20 40 60 80

Percent occupancy30

Real world Simulation

Loop station @ 2.99

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Evaluation of CalibrationEvaluation of Calibration (I) (I)

• Measure for goodness of fit: – Mean Abstract Percentage Error (MAPE)

tobssimobs tMtMtM

))(/))()(((1

Comparison of observed and simulated travel time of SB / NB I-405

6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00

simulation observation

6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00

simulation observation

3.1% (SB) 8.5% (NB)

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Evaluation of CalibrationEvaluation of Calibration (II) (II)

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

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

405S3.31ml-sim 405S3.31ml-real

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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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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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

A Calibration Procedure for Microscopic Traffic Simulation Lianyu Chu, University of California,...

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