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He Says vs. She Says Model Validation and Calibration. Kevin Chang HNTB Corporation kchang@hntb.com. Model Validation and Calibration Keys to a Successful Simulation Model Model Validation Concept Stories on Model Validation Lessons Learned. Contents.

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he says vs she says model validation and calibration

He Says vs. She SaysModel Validation and Calibration

Kevin Chang

HNTB Corporation

kchang@hntb.com

contents
Model Validation and Calibration
  • Keys to a Successful Simulation Model
  • Model Validation Concept
  • Stories on Model Validation
  • Lessons Learned
Contents
model validation and calibration
CALIBRATION – an iterative procedure to fine tune model parameters and settings so that the model can achieve what the modeler wants it to perform.
  • VALIDATION – an analytical process to verify if the model’s behavior and output statistics can truly represent actual traffic system operations.
  • PURPOSE – to have a valid simulation model that is able to generate representative numerical results that replicate traffic operations in the modeled network for analyses.
Model Validation and Calibration
facts
Model Calibration
    • Results may be limited by the tool used
    • Modeler’s knowledge of the simulation tool
    • Usually the most time consuming process
  • Model and Model Validation
    • Law and Kelton (1991) : “a simulation model of a complex system can only be an approximation to the actual system.”
    • Pegden et al. (1995) : “no model can ever be absolutely correct”. “A model is created for a specific purpose, and its adequacy or validity can only be evaluated in terms of that purpose.”
    • Fu, M : “Model validation is more an art work than science.”
Facts
keys to a successful model
Use the Right Tool
  • Modeler’s knowledge on the
    • System: traffic environment, operations, controls, management, etc.
    • Tools used
    • Issues to be addressed
  • Data availability
    • Usually the most critical element : availability and accuracy
  • Model Validation
    • Right people to review validation results
    • Selection of validation objects and focus on objectives
    • Art of work
    • Sometimes good luck
KEYS TO A SUCCESSFUL MODEL
model validation concept

Actual System

Highway/Freeway Network

Traffic Flow

Traffic Operations

Modeled System

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  • Model Logic
  • - Vehicle movements and interactions
  • Traffic assignment, routing decision
  • Weaving, merging, lane change
  • Queuing and delay
  • Traffic controls and managements
  • etc.

Input Data

Output Statistics

Model Validation Concept
stories on model validation
Inaccurate traffic demand for model inputs
    • Demand vs. flows (throughputs)
  • Incompatible performance measures
    • Delays: definition and collection
    • LOS criteria
    • Average, maximum, 90th percentile, etc.
  • Inconsistent data
    • Data collected at different times, locations, methods, etc.
  • Validating MOE against MEMORY
    • Usually best or worst scenario will be memorized
    • Always talk to the right person with field experience
  • MOE selection
    • Quantifiable and collectable with a clear definition: queue length, delay vs. speed, link density
STORIES ON MODELVALIDATION
lessons learned
Know your tools
  • Clear about project issues, system environment, scope of work
  • Always budget for data collection and analysis
  • Select “right” MOEs for model validation and presentation
  • Talk to the right person
  • Upgrade hardware and update software –be sensitive to the time required to run your models and to get output stats
  • Art of work and good luck
LESSONS LEARNED