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

2026-08-05

AI Summary

This study investigates how environmental and temporal factors nonlinearly influence daily demand flows of shared micromobility between specific origin–destination (OD) pairs. Leveraging 158,401 OD-day observations across 50 zones in Venice over two years, the research integrates environmental variables—such as temperature, precipitation, and PM10 concentrations—and, for the first time, treats individual OD connections as the fundamental analytical unit. A generalized additive mixed model (GAMM) is employed to uncover the underlying demand dynamics. Key findings reveal a significantly non-monotonic relationship between temperature and trip demand, a strong suppressive effect of heavy rainfall on usage, and seasonally heterogeneous impacts of PM10. Notably, Lido Island exhibits persistently high demand during August and September. The study proposes an “avoidance-adoption” mechanism, offering deeper insights into the spatiotemporal behavior of micromobility users.

Abstract

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.

Problem

• shared micro-mobility
• origin-destination flows
• environmental factors
• mobility demand
• temporal patterns

Innovation

• generalised additive mixed model
• origin-destination flows
• environmental sensitivity
• shared micro-mobility
• nonlinear association