arXiv subjects

Stefano Mazzuco

Publications and source records attributed to Stefano Mazzuco.

6 recordsLinked to original sources

arXiv2026

Analyzing the daily flows: Exploring shared micro-mobility factors in Venice

Shared micro-mobility has emerged as a key component of a sustainable urban transportation system, however, limited research exists on how environmental factors influence the mobility demand between specific origin-destination (OD) locations. This work extends research on the demand-side perspective to explore how temporal and environmental conditions shape daily shared micro-mobility flows in Venice. The study analyses repeated variation across 158,401 OD-day observations for two years in 50 spatial zones. Daily temperature, rainfall, and PM10 concentrations are linked to each OD-day observation while accounting for vehicle-pass composition and temporal patterns. Here, the unit of analysis is the connection between OD pairs. A generalised additive mixed model (GAMM) is used to represent the non-linearity of environmental relationships across seasons, providing a flexible framework for understanding how climatic conditions influence sustainable mobility behaviour. The results show a significant nonlinear association between temperature and mobility demand across seasons. High rainfall is associated with reduced demand, with larger reductions under moderate and heavy rainfall than on dry days. The relationship between PM10 and shared mobility use was season-dependent, creating an avoidance-versus-adoption mechanism rather than a monotonic association. After adjustment for environmental and temporal factors, a recurring increase in demand within the Lido Islands during August and September remained evident, highlighting a location-specific mobility pattern across two years. The study highlights the importance of environmental sensitivity in shared micro-mobility research. This work illustrates that the adoption of shared bikes and electric bikes depends not only on service availability but also on usage patterns, which are affected by external conditions.

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arXiv2026

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.

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arXiv2023

Functional concurrent regression with compositional covariates and its application to the time-varying effect of causes of death on human longevity

Multivariate functional data that are cross-sectionally compositional data are attracting increasing interest in the statistical modeling literature, a major example being trajectories over time of compositions derived from cause-specific mortality rates. In this work, we develop a novel functional concurrent regression model in which independent variables are functional compositions. This allows us to investigate the relationship over time between life expectancy at birth and compositions derived from cause-specific mortality rates of four distinct age classes, namely 0--4, 5--39, 40--64 and 65+ in 25 countries. A penalized approach is developed to estimate the regression coefficients and select the relevant variables. Then an efficient computational strategy based on an augmented Lagrangian algorithm is derived to solve the resulting optimization problem. The good performances of the model in predicting the response function and estimating the unknown functional coefficients are shown in a simulation study. The results on real data confirm the important role of neoplasms and cardiovascular diseases in determining life expectancy emerged in other studies and reveal several other contributions not yet observed.

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arXiv2021

Dynamic modeling of mortality via mixtures of skewed distribution functions

There has been growing interest on forecasting mortality. In this article, we propose a novel dynamic Bayesian approach for modeling and forecasting the age-at-death distribution, focusing on a three-components mixture of a Dirac mass, a Gaussian distribution and a Skew-Normal distribution. According to the specified model, the age-at-death distribution is characterized via seven parameters corresponding to the main aspects of infant, adult and old-age mortality. The proposed approach focuses on coherent modeling of multiple countries, and following a Bayesian approach to inference we allow to borrow information across populations and to shrink parameters towards a common mean level, implicitly penalizing diverging scenarios. Dynamic modeling across years is induced trough an hierarchical dynamic prior distribution that allows to characterize the temporal evolution of each mortality component and to forecast the age-at-death distribution. Empirical results on multiple countries indicate that the proposed approach outperforms popular methods for forecasting mortality, providing interpretable insights on the evolution of mortality.

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arXiv2020

Analyzing Cause-Specific Mortality Trends using Compositional Functional Data Analysis

We study the dynamics of cause--specific mortality rates among countries by considering them as compositions of functions. We develop a novel framework for such data structure, with particular attention to functional PCA. The application of this method to a subset of the WHO mortality database reveals the main modes of variation of cause--specific rates over years for men and women and enables us to perform clustering in the projected subspace. The results give many insights of the ongoing trends, only partially explained by past literature, that the considered countries are undergoing. We are also able to show the different evolution of cause of death undergone by men and women: for example, we can see that while lung cancer incidence is stabilizing for men, it is still increasing for women.

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arXiv2020

What can we learn from functional clustering of mortality data? An application to HMD data

In most cases, mortality is analysed considering summary indicators (e.~g. $e_0$ or $e^{\dagger}_0$) that either focus on a specific mortality component or pool all component-specific information in one measure. This can be a limitation, when we are interested to analyse the global evolution of mortality patterns without loosing sight of specific components evolution. The paper analyses whether there are different patterns of mortality decline among developed countries, identifying the role played by all the mortality components. We implement a cluster analysis using a Functional Data Analysis (FDA) approach, which allows us to consider age-specific mortality rather than summary measures as it analyses curves rather than scalar data. Combined with a Functional Principal Component Analysis (PCA) method it can identify what part of the curves (mortality components) is responsible for assigning one country to a specific cluster. FDA clustering is applied to 32 countries of Human Mortality Database and years 1960--2010. The results show that the evolutions of developed countries follow the same pattern (with different timing): (1) a reduction of infant mortality, (2) an increase of premature mortality, (3) a shift and compression of deaths. Some countries are following this scheme and recovering the gap with precursors, others do not show signs of recovery. Eastern Europe countries are still at stage (2) and it is not clear if and when they will enter into phase (3). All the country differences relates the different timing with which countries undergo the stages identified by clusters. The cluster analysis based on FDA allows therefore a comprehensive understanding of the patterns of mortality decline for considered countries.

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