A Calibration Procedure for Microscopic Traffic Simulation

1
A Calibration Procedure for Microscopic 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

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Outline

• Introduction
• Data preparation
• Calibration
• Evaluation of the overall model
• Discussion
• Conclusion

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Introduction to Microscopic 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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Objective

• 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 network
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Data 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 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 gt 5

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Calibration procedure
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Determining number of runs

• µ, d
• mean and std of MOE based on the already
  conducted simulation runs
• e allowable error
• 1-a confidence interval

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Step 1/2 Calibration of 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 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 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 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 lt 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 lt 5

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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 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 profiles
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Fine-tuning OD profiles

• Optimization objectives
• Min (Generalized Least Square of traffic counts
  between observed and simulated counts over all
  points and time slices)
• step 1minimizing deviation of peak hour (7-8 AM)
• criteria more than 85 of the GEH values lt 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-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 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 profiles
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volume-occupancy curve
Loop station _at_ 2.99
Real world
Simulation
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Evaluation of Calibration (I)

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

3.1 (SB) 8.5 (NB)
Comparison of observed and simulated travel time
of SB / NB I-405
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Evaluation of Calibration (II)
5-min traffic count calibration at major freeway
measurement locations (Mean Abstract Percentage
Error 5.8 to 8.7)
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Discussion

• 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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Conclusion

• 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