Complete the Data Gap

Complete Streets are easier to plan and build with more complete data. In some cases, basic roadway data such as traffic volume or the presence of sidewalks is unavailable or incomplete. In other cases, data can be skewed demographically. To close these gaps, we encourage creative methods to develop national datasets using novel and scalable approaches to data science such as utilizing big data, internet-of-things (IoT), artificial intelligence, computer vision and machine learning to a range of data sources and types that may include satellite, aerial, or street-level video and still photography; satellite, aerial, or street-level lidar data; sensor-based and crowdsourced data; vehicle probe data and telemetry, including shared micromobility and transit modes; multimodal volume/count data; and other sources and types.

Develop a Decision Support Tool

The aim is to develop an interactive decision support tool for state and local transportation agencies where users can make queries or input boundary conditions that dynamically generate maps and other visualizations that reflect insights, answer questions, and help enable complete street improvements.

Who are the expected users of this tool?
The decision support tools should be poised for commercialization and primarily serve the needs of a broad cross-section of public, private, academic, and non-profit users.