The StreetSavvi project is an effort to bring the power of AI, Digital Twin technology, advanced simulation and visualization to provide stakeholders with a powerful tool for urban development. In an ever-increasing Digital Age, the US DOT has put great effort into developing a large number of databases that encompass infrastructure, travel behavior and safety, and context as a way to answer the formidable challenge of moving people and goods in a safe and efficient way. Interestingly, commercial satellite and street imagery such as those produced by Google and other geospatial data providers emerge as an opportunity for an up-to-date inventory of transportation infrastructure. In addition, the acceleration of Artificial Intelligence through Machine Learning and its inference processes for data processing or the Large Language Models generative processes offer phenomenal opportunities to take a global approach to the field of transportation. In this context, StreetSavvi, an advanced Digital Twin development platform, applies AI computer vision processes to geospatial data, namely satellite imagery, street imagery, and camera data, to develop photorealistic 3D models of neighborhoods and develop a scalable, efficient, and user-centric digital twin platform for urban transportation planning. This project expands on the installation of speed cameras on the Roosevelt Blvd. Simulation and visualization tools allow the research team to partner with local stakeholders to assess the impact of various designs on multiple metrics such as traffic congestion, safety, cost. The StreetSavii framework provides a friendly AI based user interface for ideation to help with street design.

Infrastructure Assets: Highway Assets
Resource Types: Research Report
Capabilities: Data & Information Systems
Management Processes: Performance Based Planning & Programming