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

TL;DR

This work tackles the challenge of dynamic origin-destination demand estimation (DODE) on large-scale networks by incorporating high-resolution satellite imagery as a city-wide, class-specific density observation to complement traditional ground sensors. It develops a computational-graph framework that couples a computer-vision pipeline for class-specific vehicle density extraction from 30 cm satellite imagery with a mesoscopic dynamic network loading (DNL) model that explicitly accounts for parking and moving vehicles via separate DAR mappings. The DODE objective integrates counts, speeds, and satellite-derived densities to regularize estimation, and a forward-backward algorithm enables efficient gradient-based calibration. Across toy and real-world Pittsburgh networks, the approach yields improved density fits and more plausible OD patterns, especially on links lacking local detectors, and demonstrates faster, more stable convergence than a PC-SPSA baseline. Sensitivity analyses show robustness to moderate density-sensing errors and highlight the value of higher-frequency imagery for accurate calibration, indicating strong practical potential for city-wide dynamic traffic modeling and policy analysis.

Abstract

This study presents a novel integrated framework for dynamic origin-destination demand estimation (DODE) in multi-class mesoscopic network models, incorporating high-resolution satellite imagery together with conventional traffic data from local sensors. Unlike sparse local detectors, satellite imagery offers consistent, city-wide road and traffic information of both parking and moving vehicles, overcoming data availability limitations. To extract information from imagery data, we design a computer vision pipeline for class-specific vehicle detection and map matching, generating link-level traffic density observations by vehicle class. Building upon this information, we formulate a computational graph-based DODE framework that calibrates dynamic network states by jointly matching observed traffic counts/speeds from local sensors with density measurements derived from satellite imagery. To assess the accuracy and robustness of the proposed framework, we conduct a series of numerical experiments using both synthetic and real-world data. The results demonstrate that supplementing traditional data with satellite-derived density significantly improves estimation performance, especially for links without local sensors. Real-world experiments also show the framework's potential for practical deployment on large-scale networks. Sensitivity analysis further evaluates the impact of data quality related to satellite imagery data.

Figures (26)

• Figure 1: Model Overview
• Figure 2: Traffic condition extracted from cumulative curves
• Figure 3: Computational graph of DODE
• Figure 4: Small network
• Figure 5: Comparison of count estimation for unobserved links