Uncovering the Community Structure and Evolutionary Dynamics of On-demand Instant Delivery Networks

On-demand instant delivery has increasingly become an integral component of urban logistic systems. Yet, the dynamic mobility pattern of the vast rider fleet introduces significant sustainability challenges. Mitigating these impacts hinges on adaptive transport management grounded in the delivery system's inherent daily regularities; however, this dynamic mobility landscape remains largely underexplored. Here, using a large-scale dataset from Beijing, we construct time-evolving instant delivery networks and employ a cross-time-layer community detection method to track the hourly evolution of their dynamic community structures. Our analysis identifies 160 distinct communities and profiles their life cycles, from emergence to dissolution. By examining node variability within these dynamic communities and linking them to spatial factors through machine learning model, we find that building density and residential population significantly enhance community stability, while the presence of online retail and service facilities increases variability, highlighting the mobility patterns shaped by the tension of both dynamic demand and stable supply. These findings reveal the underlying structure of delivery mobility, providing a data-driven framework for a crucial policy shift from static rules to adaptive management. We demonstrate this transition by strategies such as allocating delivery space based on identified temporal rhythms and optimizing fleet operations within the boundaries of these dynamic communities, ultimately fostering more efficient and sustainable urban logistics solutions.