Publication Details
Overview
 
 
Paolo Speziali, Mehrdad Asadi, Diederik M. Roijers, Ann Nowe
 

Chapter in Book/ Report/ Conference proceeding

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.

Reference