Meta-analysis of choice set generation effects on route choice model estimates and predictions

Carlo Giacomo Prato

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    Large scale applications of behaviorally realistic transport models pose several challenges to transport
    modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment
    equilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generate
    choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper
    proposes a methodology and an experimental setting to provide general indications about objective judgments for
    an effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network
    to generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulated
    predicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied for
    model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models
    estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach
    allows synthesizing the effect of judgments for the implementation of path generation techniques, since a large number
    of models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysis
    estimates suggest that transport modelers should implement stochastic path generation techniques with average variance
    of its distribution parameters and correction for unequal sampling probabilities of the alternative routes in order
    to obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’
    and prediction of ‘postulated predicted routes’.
    Original languageEnglish
    JournalTransport
    Volume27
    Issue number3
    Pages (from-to)286-298
    ISSN1648-4142
    Publication statusPublished - 2012

    Keywords

    • path-based route choice modeling
    • meta-analysis
    • path generation
    • model estimation
    • model prediction
    • large scale model applications
    • path size correction
    • logit structure

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