Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models
In transport demand analysis, the calibration of a model means estimation of its (endogenous) parameters from observed data with an inference statistical estimator. Indeed, these considerations apply to any choice behaviour model, such as those derived from Random Utility Theory or any other choice...
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/5596292 |
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| author | Giulio Erberto Cantarella Antonino Vitetta |
| author_facet | Giulio Erberto Cantarella Antonino Vitetta |
| author_sort | Giulio Erberto Cantarella |
| collection | DOAJ |
| description | In transport demand analysis, the calibration of a model means estimation of its (endogenous) parameters from observed data with an inference statistical estimator. Indeed, these considerations apply to any choice behaviour model, such as those derived from Random Utility Theory or any other choice modelling theory. Calibration of choice models can be carried out from disaggregate vs. aggregate data, while inference statistical estimators can be specified through Bayesian vs. Classic (or Frequentist) approaches. In this paper, the resulting Bayesian or Classic disaggregate or aggregate calibration methods are discussed, analysed in detail, and compared from the mathematical point of view. These methods are applied to calibrate Logit choice models for describing path choice behaviour at national scale on a small sample. The Logit choice model can be derived from Random Utility Theory (or be considered an instance of the Bradley–Terry model). Path choice set definition is also discussed, and specialised indicators are used for result comparison. The main contributions of this study concern the use of two different estimation approaches, Bayesian vs. Classic, adopting and introducing some indicators of goodness of estimation. The results of this work, relating to the sample of users adopted, show that the Bayesian approach provides a better estimate than the Classic approach because the calibrated parameters are more stable, the specific constants of the alternatives decrease, and the resulting models show better values of clearly right indicator. |
| format | Article |
| id | doaj-art-5b2f3f6057f347748bda6df923ca5209 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-5b2f3f6057f347748bda6df923ca52092025-08-20T03:35:00ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/5596292Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice ModelsGiulio Erberto Cantarella0Antonino Vitetta1Dipartimento di Ingegneria CivileDipartimento di ingegneria dell’InformazioneIn transport demand analysis, the calibration of a model means estimation of its (endogenous) parameters from observed data with an inference statistical estimator. Indeed, these considerations apply to any choice behaviour model, such as those derived from Random Utility Theory or any other choice modelling theory. Calibration of choice models can be carried out from disaggregate vs. aggregate data, while inference statistical estimators can be specified through Bayesian vs. Classic (or Frequentist) approaches. In this paper, the resulting Bayesian or Classic disaggregate or aggregate calibration methods are discussed, analysed in detail, and compared from the mathematical point of view. These methods are applied to calibrate Logit choice models for describing path choice behaviour at national scale on a small sample. The Logit choice model can be derived from Random Utility Theory (or be considered an instance of the Bradley–Terry model). Path choice set definition is also discussed, and specialised indicators are used for result comparison. The main contributions of this study concern the use of two different estimation approaches, Bayesian vs. Classic, adopting and introducing some indicators of goodness of estimation. The results of this work, relating to the sample of users adopted, show that the Bayesian approach provides a better estimate than the Classic approach because the calibrated parameters are more stable, the specific constants of the alternatives decrease, and the resulting models show better values of clearly right indicator.http://dx.doi.org/10.1155/2023/5596292 |
| spellingShingle | Giulio Erberto Cantarella Antonino Vitetta Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models Journal of Advanced Transportation |
| title | Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models |
| title_full | Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models |
| title_fullStr | Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models |
| title_full_unstemmed | Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models |
| title_short | Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models |
| title_sort | bayesian disaggregate and aggregate calibration of path logit choice models |
| url | http://dx.doi.org/10.1155/2023/5596292 |
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