Highlights Heterogeneity across drivers in their response to stimuli was investigated. A random coefficient car-following model was calibrated by using an EM algorithm. Any starting point succeeded in leading to the optimal solution. Sensitivity coefficients were found to fluctuate largely, and to be strongly correlated with each other.

    Abstract As computing capabilities have advanced, random coefficient models have emerged as the mainstream method of dealing with traveler behaviors in transport studies. Car-following models with random coefficients, however, are rarely used, although many kinds of car-following models have been attempted. For the present study, we proposed a rigorous methodology to calibrate a GM-type car-following model with random coefficients, which could account for the heterogeneity across drivers who respond differently to stimuli. To avert both the curse of dimensionality and the lack of empirical identification, which can be a part of dealing with a simulated likelihood, a robust algorithm called the expectation–maximization (EM) was adopted. The calibration results confirmed that random coefficients of the model fluctuated considerably across drivers, and were correlated with each other. The exclusion of these facts might be a potential reason for the difficulty in simulating real traffic situations based on a single car-following model with constant coefficients.


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    Title :

    Identifying driver heterogeneity in car-following based on a random coefficient model


    Contributors:
    Kim, Ikki (author) / Kim, Taewan (author) / Sohn, Keemin (author)


    Publication date :

    2013-08-12


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English