Meta-analysis of choice set generation effects on route choice model estimates and predictions
Publication: Research - peer-review › Journal article – Annual report year: 2012
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Meta-analysis of choice set generation effects on route choice model estimates and predictions. / Prato, Carlo Giacomo.
In: Transport, Vol. 27, No. 3, 2012, p. 286-298.Publication: Research - peer-review › Journal article – Annual report year: 2012
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TY - JOUR
T1 - Meta-analysis of choice set generation effects on route choice model estimates and predictions
AU - Prato,Carlo Giacomo
PY - 2012
Y1 - 2012
N2 - Large scale applications of behaviorally realistic transport models pose several challenges to transportmodelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignmentequilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generatechoice sets by selecting a path generation technique and its parameters according to personal judgments. This paperproposes a methodology and an experimental setting to provide general indications about objective judgments foran effective route choice set generation. Initially, path generation techniques are implemented within a synthetic networkto generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulatedpredicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied formodel estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of modelsestimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approachallows synthesizing the effect of judgments for the implementation of path generation techniques, since a large numberof models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysisestimates suggest that transport modelers should implement stochastic path generation techniques with average varianceof its distribution parameters and correction for unequal sampling probabilities of the alternative routes in orderto obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’and prediction of ‘postulated predicted routes’.
AB - Large scale applications of behaviorally realistic transport models pose several challenges to transportmodelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignmentequilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generatechoice sets by selecting a path generation technique and its parameters according to personal judgments. This paperproposes a methodology and an experimental setting to provide general indications about objective judgments foran effective route choice set generation. Initially, path generation techniques are implemented within a synthetic networkto generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulatedpredicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied formodel estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of modelsestimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approachallows synthesizing the effect of judgments for the implementation of path generation techniques, since a large numberof models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysisestimates suggest that transport modelers should implement stochastic path generation techniques with average varianceof its distribution parameters and correction for unequal sampling probabilities of the alternative routes in orderto obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’and prediction of ‘postulated predicted routes’.
KW - path-based route choice modeling
KW - meta-analysis
KW - path generation
KW - model estimation
KW - model prediction
KW - large scale model applications
KW - path size correction
KW - logit structure
M3 - Journal article
VL - 27
SP - 286
EP - 298
JO - Transport
T2 - Transport
JF - Transport
SN - 1648-4142
IS - 3
ER -