Towards distribution-based calibration for traffic simulation

Traffic simulation models have seen increasing use during the past decades. One of the biggest challenges related to their successful application is effective calibration and validation. Emerging data collection techniques provide richer data that can be used to improve this process. In this research, we explore the use of distributions of collected data (such as accelerations, using opportunistic sensors, such as smart-phone accelerometers) for calibration purposes. The performance of the considered ubiquitous sensors is benchmarked against reference equipment, to evaluate its accuracy under different conditions. A methodology is proposed for the integration of distributions of data in traffic simulation model calibration and validation.