Toward explainable clustering for distinguishing traveler profiles

Abstract

This study proposes a novel interpretable clustering framework to identify traveler profiles that can support the design of sustainable transport policies. The approach introduces an unsupervised tree-structured clustering method that explicitly incorporates contextual feature subsets into the clustering process. By organizing features into predefined subsets and assigning them to successive layers of a tree, the method enables domain knowledge to guide how traveler profiles are constructed. At each node, the next clusters are generated using agglomerative hierarchical clustering with distance measures suited to numerical, categorical, or mixed data. A genetic algorithm is then used to explore and optimize the space of possible tree structures by maximizing a weighted average silhouette score. Applied to data from the Netherlands Mobility Panel, the framework produces traveler profiles defined by travel behavior and sociodemographic characteristics while maintaining interpretability in the clustering process.