Paolo Speziali, Mehrdad Asadi, Diederik M. Roijers, Ann Nowe
Personalized pedestrian routing can be framed as an instance of AI-driven adaptive routing, where the system learns user preferences and adjusts route recommendations accordingly. However, modeling heterogeneous route-choice preferences between users and travel contexts remains challenging, as fully individualized models are difficult to scale. We propose a context-aware routing framework that captures this heterogeneity through a small number of interpretable subgroup models. We apply Exceptional model mining to identify subgroups of observations exhibiting statistically distinct decision patterns relative to a global preference model. Our approach enables prediction through shared subgroup-specific preference models rather than individual optimization. Using simulated user models for context-dependent route choice decisions, our experimental results show that our approach successfully identifies meaningful context-dependent preference groups, supporting scalable and interpretable personalized pedestrian routing.
Speziali, P, Asadi, M, Roijers, DM & Nowe, A 2026, When Context Matters: Exceptional Model Mining for Pedestrian Route Choice. in M Alimardani, T Lenaerts, A Meyer-Vitali, A Nowe, J Vennekens & S Wang (eds), Proceedings of the Fifth International Conference on Hybrid Human-Machine Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 423, IOS Press, pp. 477-480. https://doi.org/10.3233/FAIA260539
Speziali, P., Asadi, M., Roijers, D. M., & Nowe, A. (2026). When Context Matters: Exceptional Model Mining for Pedestrian Route Choice. In M. Alimardani, T. Lenaerts, A. Meyer-Vitali, A. Nowe, J. Vennekens, & S. Wang (Eds.), Proceedings of the Fifth International Conference on Hybrid Human-Machine Intelligence (pp. 477-480). (Frontiers in Artificial Intelligence and Applications; Vol. 423). IOS Press. https://doi.org/10.3233/FAIA260539
@inproceedings{bcbf6621a46649249736e438deb52e14,
title = "When Context Matters: Exceptional Model Mining for Pedestrian Route Choice",
abstract = "Personalized pedestrian routing can be framed as an instance of AI-driven adaptive routing, where the system learns user preferences and adjusts route recommendations accordingly. However, modeling heterogeneous route-choice preferences between users and travel contexts remains challenging, as fully individualized models are difficult to scale. We propose a context-aware routing framework that captures this heterogeneity through a small number of interpretable subgroup models. We apply Exceptional model mining to identify subgroups of observations exhibiting statistically distinct decision patterns relative to a global preference model. Our approach enables prediction through shared subgroup-specific preference models rather than individual optimization. Using simulated user models for context-dependent route choice decisions, our experimental results show that our approach successfully identifies meaningful context-dependent preference groups, supporting scalable and interpretable personalized pedestrian routing.",
author = "Paolo Speziali and Mehrdad Asadi and Roijers, \{Diederik M.\} and Ann Nowe",
note = "Publisher Copyright: {\textcopyright} 2026 The Authors.",
year = "2026",
month = jul,
day = "2",
doi = "10.3233/FAIA260539",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "477--480",
editor = "Maryam Alimardani and Tom Lenaerts and Andre Meyer-Vitali and Ann Nowe and Joost Vennekens and Shenghui Wang",
booktitle = "Proceedings of the Fifth International Conference on Hybrid Human-Machine Intelligence",
address = "Netherlands",
}