Unlike a traditional fixed parameters logit model, the computation of marginal effects (MEs) for the random parameter’s logit model is much more complex. In this study, a random parameters logit with heterogeneity in means and variances model was estimated using crash-severity data. Three computing methods for MEs based on global means, individual estimates, and Monte Carlo simulation of random parameters were proposed. The results, along with the software-reported MEs, were comprehensively compared. Results indicate that (1) enormous bias was detected in software-reported MEs; (2) simply using means of random parameters also produces bias; (3) the Monte Carlo simulation was most likely an effective way to computing MEs; and (4) the individual estimates method may also be reliable as the random parameters distribution has been well captured. Methods provided by this study can prompt the proper application of random parameters approach for road safety and other traffic scenarios.


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    Title :

    Marginal Effects for Random Parameters Logit Models: A Case Study of Crash Severity Analysis


    Contributors:

    Conference:

    22nd COTA International Conference of Transportation Professionals ; 2022 ; Changsha, Hunan Province, China


    Published in:

    CICTP 2022 ; 1777-1788


    Publication date :

    2022-09-08




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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