Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data

This study presents a novel integrated framework for dynamic origin-destination demand estimation (DODE) in multi-class mesoscopic network models, leveraging high-resolution satellite imagery together with conventional tra ffic data from local sensors. To extract information from imagery data, we design a computer vision pipeline for class-specific vehicle detection and map matching, generating link-level tra ffic density observations by vehicle class. Building upon this information, we formulate a computational graph-based DODE model that calibrates dynamic network states by jointly matching observed tra ffic counts and travel times from local sensors with density measurements derived from satellite imagery. To assess the accuracy and scalability of the proposed framework, we conduct a series of numerical experiments using both synthetic and real-world data. The results of out-of-sample tests demonstrate that supplementing traditional data with satellite-derived density significantly improves estimation performance, especially for links without local sensors. Real-world experiments also confirm the framework's capability to handle large-scale networks, supporting its potential for practical deployment in cities of varying sizes. Sensitivity analysis further evaluates the impact of data quality related to satellite imagery data. Introduction The widespread availability of spatio-temporal data has created new opportunities for advancing computational tools to model network flows, individual traveler behavior, and travel demand in dynamic transportation networks. Recent developments in sensing technologies and artificial intelligence are revolutionizing traditional models, making them more data-driven, scalable, and e ff ective for complex, large-scale networks. Dynamic Origin-destination Demand Estimation (DODE) is a foundational prerequisite for dynamic network models to accurately reproduce the status quo spatio-temporal network conditions, supporting tra ffic assignment (Pi et al. 2019) and control strategies (Y e et al. 2019, Liu, Ma & Qian 2023, Ke et al. 2025). DODE studies can be broadly categorized into model-based methods, which embed physics-informed tra ffic assignment models, and model-free methods, which formulate the problem using data-driven techniques without tra ffic assignment constraints.