Steady-State Solutions and Multiclass Calibration of Gipps Microscopic Traffic Flow Model

Abstract

This study addresses the problem of calibration of the Gipps microscopic traffic flow model. The approach consists of first deriving traffic stream models, in the form of steady-state solutions of car-following models, and then fitting such models to stationary traffic data. To this end, traffic stream models for the Gipps model were first attained, and an explicit formula for the flow at capacity as a function of microscopic parameters is provided. Analysis of the models for different combinations of microscopic parameters explains the widely held belief that the Gipps model is unable to reproduce unstable traffic phenomena. To be suitable for model parameter calibration in simulation practice, single-class models were generalized to a multiclass traffic scenario for which a calibration procedure was developed. Once applied to real motorway traffic data, the multiclass scenario proved its effectiveness in terms of error statistics. Values of calibrated parameters were all significant and consistent with expectations. Moreover, they were consistent with the observed aggregate measures (e.g., flow at capacity). Finally, unlike nonstationary, model-based approaches, the computing time required by the multiclass calibration presented is negligible, allowing calibration of a large number of parameters, that is, calibration of different classes of vehicles.