Micro-simulation of Traffic - Examples & Applications

Example

A base model predicts an average corridor travel time of 8.1 min8.1\text{ min} while field observations give 9.0 min9.0\text{ min}. Determine the percentage error and explain the next calibration step.

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Example

Ten random-seed runs of an alternative produce mean delay values with an average of 46.2 s/veh46.2\text{ s/veh} and a sample standard deviation of 3.6 s/veh3.6\text{ s/veh}. Estimate the standard error of the mean.

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Example

A calibrated existing-condition model reproduces field counts closely, but the validation dataset shows simulated maximum queues that are consistently 35% shorter than observed. Can the model be accepted solely because link volumes match?

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Key Takeaways
  • Microsimulation calibration is an evidence-based model-identification task, not visual tuning.
  • Validation must use independent observations and multiple performance measures.
  • Stochastic models require multiple random seeds and statistical interpretation.
  • A believable animation is not sufficient evidence that a model is valid.