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

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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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
Publication date2012
Volume27
Issue3
Pages286-298
ISSN1648-4142
StatePublished

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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