Abstract:
Calibrating and validating traffic models is a process that depends on field data that are often limited, but essential for determining inputs to the model and for assessing its reliability. A quantification and systemization of the calibration/validation process exposes statistical issues inherent in the use of such data. Formalization of the calibration/validation process leads naturally to the use of Bayesian methodology for assessing uncertainties in model predictions arising from a multiplicity of sources especially statistical variability in estimating and calibrating input parameters and model discrepancy. In an earlier paper the general problem was elucidated; in this paper we will carry out the full calibration/validation process in the context of a widely used deterministic traffic model, namely the Highway Capacity Manual (HCM) model for control delay at signalized intersection approaches. In particular we are able to assess the reliability of the model through quantification of the uncertainty in estimation of model parameters, predictions of model delay and predictions using data-adjustments to the model. While the methods are described in a specific context they can be used generally, inhibited at times by computational burdens that must be overcome.
Keywords:
Bayesian Analysis; Posterior Distribution; Model Calibration; Model Validation; Highway Capacity Manual; Control Delay.
tr144.pdf