Research on reduction strategy based on propagation model and logistic model

This study advances computational modeling techniques by integrating propagation and logistic models with transportation network analysis. Utilizing a dual-model approach, we explore the application of compartmental models (SIR model) and population dynamics (logistic model) to simulate and predict dynamics in a structured network. Model I (Propagation Model) employs a SIR framework to predict the spread through potential transmission routes among cities, utilizing differential equations to model infection-like spread dynamics. Model II (Logistic Model) focuses on population dynamics through a logistic growth equation, which is tailored to describe population variations over time under certain constraints. The innovative aspect of the research is further demonstrated in Model III, which incorporates traffic data (AATD) to assess how major traffic flows might influence propagation patterns. Model IV, the road traffic model, extends the analysis to explore how transportation nodes and pathways can be integral to the dissemination process, leveraging network theory to map potential spread scenarios. By repurposing epidemiological and logistic methodologies within the context of network and transportation analysis, this research not only showcases the adaptability of these models outside their conventional biological applications but also highlights their potential in tackling complex systems analysis, providing a robust framework for addressing diverse challenges through mathematical modeling. This approach exemplifies the intersection of computational modeling with practical applications, offering a blueprint for future interdisciplinary research.