The purpose of this project is to discover new continuous approximation models for public transit network design, with a specific focus on rural areas where access to transit and coverage present significant challenges. Rural transit systems face unique constraints in connecting dispersed population centers while maintaining economic viability, which necessitates a modelling approach that addresses multiple competing objectives simultaneously. The continuous approximation paradigm is a quantitative method for solving logistics problems using a small set of parameters to model a complex system, which results in simple algebraic expressions that are easier to manage than (for example) large‐scale optimization models. As a further benefit, one often obtains insights from these simpler formulations that determine what affects the outcome most significantly. Although continuous approximation models have been used for over 60 years in logistics systems analysis, there has been very little research conducted on their application to problems in rural transit networks, likely due to their distinctive spatial characteristics and coverage requirements. Recent research demonstrates that limited flexibility yields disproportionate benefits in logistics systems. This project will combine tools from geospatial optimization, computational geometry, and geometric probability theory to formulate new models that will solve these problems. Furthermore, these models will identify which complementary infrastructure investments would most effectively increase transit availability and ridership in rural counties. The research outcomes include both theoretical advances in continuous approximation methodology and practical planning tools for rural transit agencies with limited computational resources.