Attraction constraints are common and often appropriate for destination choice models, including, but not limited to, doubly constrained gravity distribution models. Estimation of their parameters by discrete-choice methods, however, seldom accounts for the constraints, despite bias known from their omission. For multinomial logit destination choice, this paper first formulates the parameter estimation for maximum likelihood of a set of observations, subject to exogenous attraction constraints on the model’s application to a population. Second, this formulation is extended for attraction constraints being linear combinations of size variables with coefficients to be estimated. Both the utility and size parameter estimations are distinct from the well-known formulation for unconstrained estimation, because of the contribution of the constraining shadow-price utilities to the likelihood gradients. Gradients and the Hessian are identified, along with adaptations of them for Newton-Raphson parameter updates and Cramér-Rao bounds. Experiments demonstrate its computational tractability and performance, and compare its estimations with those of unconstrained and other methods.


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    Title :

    Maximum Likelihood Estimation of Destination Choice Models with Constrained Attractions


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Gibb, John (author)


    Publication date :

    2022-11-11




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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