StreetOps: An Open Computational Framework for Integrating Street Networks and Urban Operations into Adaptive Urban Design

Urban planning has developed a rich computational ecosystem for acquiring spatial data, representing street networks, analyzing movement, modeling travel demand, measuring accessibility, and simulating transportation systems. Yet these tools generally terminate at description, diagnosis, or simulation. The final movement from observed urban operations to physical, regulatory, and programmatic design remains largely ad hoc, difficult to reproduce, and dependent on expert interpretation that is rarely encoded or reported. This article proposes StreetOps, an open computational framework for translating urban operations into design. StreetOps is conceived as a compositional layer built on existing open-source geospatial tools and standards rather than as a replacement for them. Its central technical abstraction, the OpsGraph, represents infrastructure, activities, temporal conditions, operating rules, public priorities, conflicts, and candidate interventions within a common time-aware and rule-aware model. The framework organizes analysis into six stages: acquire, integrate, diagnose, translate, generate scenarios, and evaluate. A proof-of-concept application to autonomous-vehicle pick-up and drop-off activity in Hayes Valley, San Francisco, illustrates how spatial and temporal sorting can be converted into targeted, adaptive curb-design responses while protecting transit, accessibility, pedestrian safety, and other public functions. The article defines the research problem, data model, translation logic, software architecture, governance principles, and development roadmap for StreetOps. Its intended contribution is to make the evidence-to-intervention step of urban planning more explicit, auditable, reusable, and reproducible.