TY - JOUR

T1 - Meta-analysis of choice set generation effects on route choice model estimates and predictions

A1 - Prato,Carlo Giacomo

AU - Prato,Carlo Giacomo

PB - Vilniaus Gedimino Technikos Universitetas Leidykla Technika

PY - 2012

Y1 - 2012

N2 - Large scale applications of behaviorally realistic transport models pose several challenges to transport<br/>modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment<br/>equilibrium problem help modelers in enhancing the route choice behavior modeling, but require them to generate<br/>choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper<br/>proposes a methodology and an experimental setting to provide general indications about objective judgments for<br/>an effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network<br/>to generate possible subjective choice sets considered by travelers. Next, ‘true model estimates’ and ‘postulated<br/>predicted routes’ are assumed from the simulation of a route choice model. Then, objective choice sets are applied for<br/>model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models<br/>estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach<br/>allows synthesizing the effect of judgments for the implementation of path generation techniques, since a large number<br/>of models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysis<br/>estimates suggest that transport modelers should implement stochastic path generation techniques with average variance<br/>of its distribution parameters and correction for unequal sampling probabilities of the alternative routes in order<br/>to obtain satisfactory results in terms of coverage of ‘postulated chosen routes’, reproduction of ‘true model estimates’<br/>and prediction of ‘postulated predicted routes’.

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

JO - Transport

JF - Transport

SN - 1648-4142

IS - 3

VL - 27

SP - 286

EP - 298

ER -