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

2025-10-21

AI Summary

Urban areas with limited data availability lack high spatiotemporal-resolution commuting origin-destination (OD) matrices, hindering scalable transportation modeling and optimization. Method: This paper proposes an automated, open-source OD synthesis framework leveraging five publicly available data sources—American Community Survey (ACS), Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES), OpenStreetMap road networks, building footprints, and county boundaries. It jointly calibrates heterogeneous census data via first-passage constrained sampling and integer programming to generate fine-grained OD flows at minute-level departure times and block-group-level spatial resolution. Contribution/Results: The pipeline enables fully customizable, privacy-preserving, and human-intervention-free OD modeling for any U.S. county. Validated in Hamilton County, Tennessee, it produces ~150,000 high-fidelity trip chains within minutes and has been successfully integrated into both classical and learning-based vehicle routing algorithm benchmarks, significantly enhancing the scalability and practicality of traffic simulation and optimization.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Data transparency and provenance

Abstract

High-resolution origin-destination (OD) tables are essential for a wide spectrum of transportation applications, from modeling traffic and signal timing optimization to congestion pricing and vehicle routing. However, outside a handful of data rich cities, such data is rarely available. We introduce MOVEOD, an open-source pipeline that synthesizes public data into commuter OD flows with fine-grained spatial and temporal departure times for any county in the United States. MOVEOD combines 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, road network information from OpenStreetMap (OSM), and building footprints from OSM and Microsoft, into a single OD dataset. We use a constrained sampling and integer-programming method to reconcile the OD dataset with data from ACS and LODES. Our approach involves: (1) matching commuter totals per origin zone, (2) aligning workplace destinations with employment distributions, and (3) calibrating travel durations to ACS-reported commute times. This ensures the OD data accurately reflects commuting patterns. We demonstrate the framework on Hamilton County, Tennessee, where we generate roughly 150,000 synthetic trips in minutes, which we feed into a benchmark suite of classical and learning-based vehicle-routing algorithms. The MOVEOD pipeline is an end-to-end automated system, enabling users to easily apply it across the United States by giving only a county and a year; and it can be adapted to other countries with comparable census datasets. The source code and a lightweight browser interface are publicly available.

Problem

• Synthesizes high-resolution origin-destination commute distribution
• Reconciles multiple open data sources for accurate commuting patterns
• Generates fine-grained spatial-temporal OD flows for transportation applications

Innovation

• Combining five open data sources into OD dataset
• Using constrained sampling and integer-programming reconciliation
• Creating automated end-to-end system for any US county