Dustin is a software engineer working to make transportation planning tools more widely accessible. He originally gained an interest in agent-based modelling and simulation from a background in creating computer games. He graduated from the undergraduate Computer Science program at the University of Texas at Austin. There he built a traffic simulation that allows a fleet of autonomous vehicles to participate in auctions to move through intersections more quickly. He then joined Google Cloud in Seattle, where he worked on backend systems for Google Compute Engine. In 2018, he started A/B Street, an open-source project to let interested citizens explore and propose changes to road networks to improve walking, cycling, and public transit. He joined the Urban Analytics team at The Alan Turing Institute in December 2021.
Dustin uses A/B Street as a rapid prototyping tool to explore how cities around the world can reduce their dependency on motor vehicles. Through this platform, he's built a simple traffic simulation, a web-based interface to reallocate road space and configure traffic signal timing, and tools to design low-traffic neighbourhoods, 15-minute neighbourhoods, and cycle networks. The goal for this work is to allow individuals, grassroot advocacy groups, local authorities, and professional planners all communicate more easily by using the same software.
Dustin is also interested in building out an ecosystem of urban digital twins, by reducing duplicate efforts for analysing transportation networks and generating synthetic populations. Initial steps in this direction include osm2lanes, a common library for understanding road-space allocation from OpenStreetMap data, and odjitter, a tool to disaggregate origin/destination data.