Highlights We propose an approach to calibrate nonlinear car-following laws from trajectories. The calibration framework reproduces time- and frequency-domain properties. We combine maximum likelihood estimation and simulated/analytical feedback. A series of experiments with field data demonstrate satisfactory performance.

    Abstract Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver’s car-following behavior but also the vehicle trajectory’s time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). We use Newell’s car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.


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    Titel :

    Calibration of nonlinear car-following laws for traffic oscillation prediction


    Beteiligte:
    Rhoades, Christine (Autor:in) / Wang, Xin (Autor:in) / Ouyang, Yanfeng (Autor:in)


    Erscheinungsdatum :

    2016-05-23


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch