Calibrating and Validating Deterministic Traffic Models

Abstract

Calibration and validation of traffic models are processes that depend on field data that are often limited but are essential for determination of inputs to the model and assessment of its reliability. Quantification and systematization of the calibration and validation process expose statistical issues inherent in the use of such data. Formalization of the calibration and validation process naturally leads to the use of Bayesian methodology for assessment of uncertainties in model predictions that arise from a multiplicity of sources, especially statistical variability in estimation and calibration of the input parameters and model discrepancy. The general problem was elucidated in an earlier paper; this paper carries out the full calibration and validation process in the context of a widely used deterministic traffic model, namely, the Highway Capacity Manual model for control delay at signalized intersection approaches. In particular, the reliability of the model was assessed through quantification of the uncertainty in the estimation of model parameters, predictions of model delay, and predictions obtained by adjusting the data used in the model. While the methods are described in a specific context, they can be used generally but are inhibited at times by computational burdens that must be overcome.