Beyond the Flow: A Bayesian Latent Clustering Framework for Shared Micro-mobility Users in Venice

The study on shared micro-mobility is based on trip modeling and user data.

User segmentation in shared micromobility systems is traditionally studied by aggregating trip-level observations into user-specific summary measures before applying clustering techniques.

Such aggregation can obscure trip-level variability and lead to ecological fallacies if results are interpreted as applying to individual records.

We propose a Bayesian finite mixture model for multivariate categorical count data that clusters users directly from repeated trip-level observations while preserving the full categorical structure of individual travel behavior.

This approach focuses on identifying heterogeneous mobility users from high-dimensional categorical trip behavior while accounting for uncertainty in cluster assignments.

Users are the fundamental unit of analysis for exploring latent cluster patterns.

The model represents each user with a product-multinomial likelihood with latent cluster membership.

The methodology is illustrated using a one-year trip record of shared bikes and e-bikes from the Municipality of Venice, Italy, comprising over 220,000 trips made by more than 11,000 recurrent users.

The analysis identifies eight distinct latent mobility profiles corresponding to localized, commuter-oriented, tourist-oriented, central, and inter-zonal travel behaviors.

The proposed framework provides a flexible and computationally scalable approach for clustering repeated categorical observations and is readily applicable to other large-scale behavioral and transportation datasets.