MoveOD: Synthesizing Origin-Destination Commute Distribution from U.S. Census Data

High-resolution origin-destination (OD) tables are critical for traffic modeling, signal timing, congestion pricing, and vehicle routing, yet such data is rarely available outside a few data-rich cities. MoveOD, developed by Rishav Sen and colleagues, addresses this gap by combining five open data sources: American Community Survey (ACS) departure time and travel time distributions, Longitudinal Employer-Household Dynamics (LODES) residence-to-workplace flows, county geometries, OpenStreetMap road networks, and building footprints from OSM and Microsoft. The pipeline uses a constrained sampling and integer-programming method to match commuter totals per origin zone, align workplace destinations with employment distributions, and calibrate travel durations to ACS-reported commute times, ensuring accurate representation of commuting patterns.

Demonstrated on Hamilton County, Tennessee, MoveOD generated roughly 150,000 synthetic trips in minutes, which were fed into a benchmark suite of classical and learning-based vehicle-routing algorithms. The end-to-end automated system requires only a county name and year to produce granular, time-dependent OD datasets across the United States, and it can be adapted to other countries with comparable census datasets. The source code and a lightweight browser interface are publicly available on GitHub, making it immediately accessible for transportation planners, researchers, and policymakers.