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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

Building Up Agency-Wide Automated Image Processing Capability to Inform Safety and Mobility

District of Columbia Department of Transportation (DDOT) frequently uses camera footage to better understand traveler behavior (e.g., around parking, lane usage, turning movements, and collisions or near misses) and existing configuration and condition of the roadway and associated infrastructure. This footage provides insight into existing patterns or conditions for future planning or design changes, provides insight into real-time conditions for operational decision making, and drives before and after analyses for assessing the impacts of a change in condition. To date, much of the processing of this footage has been done manually, which often proves costly and inefficient, thereby limiting the degree to which DDOT is able to use camera footage. Recent advances in artificial intelligence and machine learning have the potential to speed up and improve processes for analyzing camera footage, but a consistent, agency-wide approach is needed to ensure quality of analysis, maximize utility across divisions, and minimize any duplication of effort. This project will provide comprehensive recommendations on how DDOT can expand its use of camera footage via automated image processing to ensure all agency information needs are met. Results will directly identify actions and changes that DDOT can make to existing technology, policy, and processes to ensure quality of analysis, maximize utility across divisions, and minimize any duplication of effort. The project will benefit the District by enhancing DDOT’s ability to understand traveler behavior and roadway conditions toward better planning, design, and operational decision-making.