Award

Complete Streets planning is currently limited in scope by human bandwidth. Modeling and analyzing traffic infrastructure is time-consuming, and manually annotating transportation networks does not scale to large study areas. Kitware, along with NYU and MJ Engineering, proposes to build a Complete Streets AI assistant that can analyze infrastructure, answer queries, and make suggestions at scale. The foundation of this capability is an accurate traffic model; initially, we will build an accurate and complete digital traffic network capturing walkways and their interactions using state-of-the-art computer vision algorithms. In Phase I, we will extend our prior work to automatically extract walkway networks from overhead image surveys, incorporating computer vision techniques on ground-level imagery to enhance accuracy and provide finer details such as curbs and sidewalk conditions. If successful, our approach will automate the generation of walkway networks at a national scale with minimal cost. Phase II will develop an LLM assistant to interact with planners through a retrieval augmented generation framework that can respond to queries by indexing the traffic network and generating proposals. Finally, we will use our team’s web-based visualization tools to create an interface for planners to interact with the assistant and visualize proposed network alterations.