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StreetSavvi: Complete Street AI Companion for Digital Twin Driving

This project StreetSavvi is an effort to bring the power of Artificial Intelligence (AI), Digital Twin technology, advanced simulation and visualization to local Stakeholders such as the City of Philadelphia, Pennsylvania Department of Transportation (PennDOT) and SEPTA for a partnership during the Route for Change Project which will redevelop the Philadelphia Roosevelt Boulevard. 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, the team proposes the development of StreetSavvi, an advanced Digital Twin development platform, which 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 a previous Safety21 University Transportation Center feasibility project. It also expands on the installation of speed cameras on the Roosevelt Blvd. This proposed Streetsavvi project will closely follow the design alternatives developed by PennDOT, the City of Philadelphia, and SEPTA as they include components such as Neighborhood boulevard, Partially-capped expressway, Bus Rapid Transit (BRT), Light Rail Transit (LRT), Subway into the new design. The simulation and visualization tools will allow the research team to partner with the stakeholders to assess the impact of various designs on multiple metrics such as traffic congestion, safety, and cost. The virtual models will allow an efficient assessment of the proposed infrastructural changes, such as lane removals, enhanced bicycle/pedestrian facilities, and exclusive bus only lanes. The research team proposes with this project to automate the ideation and simulation process by creating a friendly AI based user interface that is easy to use by state, local, and tribal transportation agencies who are stakeholders in the design of our streets.

Language

  • English

Project

  • Status: Completed
  • Funding: $70,000.00
  • Contract Numbers:

    69A3552344811

    69A3552348316

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Performing Organizations:

    University of Pennsylvania

    ,    
  • Principal Investigators:

    Guerra, Erick

    Loeb, Helen

  • Start Date: 20250701
  • Expected Completion Date: 20260831
  • Actual Completion Date: 20260831
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 02002848
  • Record Type: Research project
  • Source Agency: Safety21 University Transportation Center
  • Contract Numbers: 69A3552344811, 69A3552348316
  • Files: UTC, RIP
  • Created Date: Sep 17 2026 3:05PM