高分辨率起讫点（Origin-Destination, OD）表格对于交通领域广泛应用至关重要，涵盖交通流建模、信号配时优化、拥堵收费及车辆路径规划等。然而，除少数数据丰富的城市外，此类数据通常难以获取。本文提出 MOVEOD——一个开源流水线系统，可将公开数据合成为美国任意县的细粒度空间与时间维度的通勤 OD 流，包括精确的出发时刻。MOVEOD 整合五类开放数据源：美国社区调查（American Community Survey, ACS）中的出发时间与通勤时长分布、纵向雇主-家庭动态调查（Longitudinal Employer-Household Dynamics, LODES）中的居住地至工作地流动数据、县级行政区划几何信息、OpenStreetMap（OSM）提供的道路网络数据，以及 OSM 与微软联合发布的建筑轮廓数据，最终生成统一的 OD 数据集。我们采用受约束的抽样与整数规划方法，使生成的 OD 数据集与 ACS 和 LODES 数据保持一致。该方法包含三个关键步骤：（1）匹配各出发分区内的通勤者总数；（2）将工作地目的地与就业分布对齐；（3）依据 ACS 报告的通勤时长校准行程持续时间。由此确保 OD 数据准确反映实际通勤模式。我们在田纳西州汉密尔顿县开展实证验证，于数分钟内生成约 15 万条合成通勤行程，并将其输入经典算法与学习型算法构成的车辆路径规划基准测试套件。MOVEOD 是一套端到端自动化的系统，用户仅需指定县名与年份即可便捷部署于全美范围；其框架亦可适配拥有类似人口普查数据的其他国家。源代码及轻量级浏览器界面均已公开发布。

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.