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Hauptverfasser: Santos-Pascual, M., Insua, D. Ríos, Angulo, P.
Format: Preprint
Veröffentlicht: 2026
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Online-Zugang:https://arxiv.org/abs/2605.06236
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author Santos-Pascual, M.
Insua, D. Ríos
Angulo, P.
author_facet Santos-Pascual, M.
Insua, D. Ríos
Angulo, P.
contents The Plackett-Luce model is widely used to deal with probabilities in discrete choice settings. This paper introduces a novel two-level Plackett-Luce model combined with a multinomial logistic scheme that provides the basis for the route choice module in a smart mobility platform. For this, we develop Bayesian inference and prediction mechanisms to capture consumers' preferences for personalized route recommendations. The model is empirically tested, allowing for refinements and discussion of its applicability. We also illustrate its practical relevance through several use cases, including relevant route selection, coordinated car pooling, incentive design and synthetic data generation.
format Preprint
id arxiv_https___arxiv_org_abs_2605_06236
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Two-Level Plackett-Luce Model for preference modeling in smart mobility platforms
Santos-Pascual, M.
Insua, D. Ríos
Angulo, P.
Applications
Methodology
The Plackett-Luce model is widely used to deal with probabilities in discrete choice settings. This paper introduces a novel two-level Plackett-Luce model combined with a multinomial logistic scheme that provides the basis for the route choice module in a smart mobility platform. For this, we develop Bayesian inference and prediction mechanisms to capture consumers' preferences for personalized route recommendations. The model is empirically tested, allowing for refinements and discussion of its applicability. We also illustrate its practical relevance through several use cases, including relevant route selection, coordinated car pooling, incentive design and synthetic data generation.
title A Two-Level Plackett-Luce Model for preference modeling in smart mobility platforms
topic Applications
Methodology
url https://arxiv.org/abs/2605.06236