Microscopic Calibration and Validation of Car-Following Models -- A Systematic Approach

Abstract

Calibration and validation techniques are crucial in assessing the descriptive and predictive power of car-following models and their suitability for analyzing traffic flow. Using real and generated floating-car and trajectory data, we systematically investigate following aspects: Data requirements and preparation, conceptional approach including local maximum-likelihood and global LSE calibration with several objective functions, influence of the data sampling rate and measuring errors, the effect of data smoothing on the calibration result, and model performance in terms of fitting quality, robustness, parameter orthogonality, completeness and plausible parameter values.

arXiv commentPresentation at the 20th ISTTT in Noordwijk, the Netherlands, 2013

References in corpus (5)

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• Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study
• Fast Calibration of Car Following models to Trajectory data using the Adjoint Method
• Identifiability of car-following dynamic
• Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach
• Driving Style Recognition at First Impression for Online Trajectory Prediction