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

2026-07-01

AI Summary

This study addresses the limitations of conventional shared micromobility user clustering, which relies on aggregated trip-level metrics and often overlooks individual behavioral heterogeneity, thereby risking ecological fallacy. To overcome this, the authors propose a Bayesian finite mixture model that directly clusters users based on trip-level multidimensional categorical count data, using a product-multinomial likelihood to preserve the full structure of individual mobility patterns with users as the fundamental analytical unit. This approach avoids aggregation bias, explicitly quantifies clustering uncertainty, and leverages a scalable inference algorithm to efficiently handle high-dimensional data. Applied to a dataset comprising 220,000 trips by 11,000 frequent users in Venice, the method identifies eight distinct mobility profiles, including local commuting, tourism-oriented travel, central-area activity, and cross-regional movement patterns.

Abstract

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.

Problem

• shared micro-mobility
• user segmentation
• trip-level clustering
• ecological fallacy
• latent mobility profiles

Innovation

• Bayesian finite mixture model
• latent clustering
• multivariate categorical count data
• shared micro-mobility
• trip-level behavior