A calibration procedure for increasing the accuracy of microscopic traffic simulation models

Abstract

Efforts to address operational issues in transportation have been the focus of many research efforts. A number of these efforts were geared toward developing microscopic traffic simulation models to accurately represent the complex and dynamic operation of a transportation network. One of the challenges with such models is that they do not always adequately reflect field conditions—particularly when representing traffic operations across different time periods. This paper presents a robust calibration procedure that aims to increase the accuracy of calibrated microscopic traffic simulation models. This procedure is based on a Monte Carlo approach to generate candidate parameter sets, which are aimed to produce calibrated simulation models. Model runs of these parameter sets are evaluated against robust calibration criteria, including startup and saturation flow characteristics and travel time distributions. The parameter sets that satisfy these criteria are considered as adequately calibrated to accurately reflect field performance measures. In applying this procedure, the results suggest that this approach offers a robust and effective method of calibrating simulation models where disaggregate-level vehicle data are available—which is becoming more prevalent with further advancements in mobile sensor and connected vehicle technologies.