@inproceedings{iccps2026_moveod,
author = {Sen, Rishav and Talusan, Jose Paolo and Dubey, Abhishek and Mukhopadhyay, Ayan and Samaranayake, Samitha and Laszka, Aron},
title = {MoveOD: Synthesizing Fine-Grained Origin--Destination Data for Transportation CPS},
year = {2026},
booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
location = {Saint Malo, France},
abstract = {High-resolution origin–destination (OD) tables are critical to cyber-physical transportation systems, enabling realistic digital twins, adaptive routing strategies, signal timing optimization, and demand-responsive mobility services. However, such OD data is rarely available outside a small number of data-rich metropolitan regions. We introduce MoveOD, an open-source pipeline that synthesizes publicly available datasets to generate fine-grained commuter OD flows with spatial and temporal departure distributions for any U.S. county. MoveOD fuses American Community Survey travel-time and departure distributions, Longitudinal Employer–Household Dynamics (LODES) residence–workplace flows, OpenStreetMap (OSM) road networks, and building footprint data. Our approach ensures consistency with observed commuter totals, workplace employment distributions, and reported travel durations. MoveOD is integrated with a transportation digital twin, enabling end-to-end CPS experimentation. We demonstrate the system in Hamilton County, Tennessee, generating approximately 150,000 synthetic daily trips and evaluating routing algorithms in a live dashboard.},
keywords = {origin-destination synthesis, travel demand, transportation planning, data fusion, Bayesian methods, public datasets, traffic simulation},
note = {Acceptance rate: 28\%; Short Paper; Track: Systems and Applications},
series = {HSCC/ICCPS '26}
}