Analysis and Comparison of Microscopic Traffic Flow Models with Real Traffic Microscopic Data

Abstract

The evermore widespread use of microscopic traffic simulation in the analysis of road systems has refocused attention on submodels, including car-following models. The difficulties of microscopic-level simulation models in the accurate reproduction of real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacies of the submodels themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty with the gathering of accurate field data. In this study, the use of kinematic differential Global Positioning System instruments allowed the trajectories of four vehicles in a platoon to be accurately monitored under real traffic conditions on both urban and extraurban roads. Some of these data were used to analyze the behaviors of four microscopic traffic flow models that differed greatly in both approach and complexity. The effect of the choice of performance measures on the model calibration results was first investigated, and intervehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared with those from previous studies (with peaks in cross validations between urban and extraurban experiments). This confirms the need for real traffic data. On comparison of the models, all models showed similar performances (i.e., similar deviations in validation). Surprisingly, however, the simplest model performed on average better than the others, but the most complex one was the most robust, never reaching particularly high deviations.