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

2025-06-25

AI Summary

To address data sparsity, poor scalability, and low estimation accuracy on detector-free road segments in Dynamic Origin-Destination Demand Estimation (DODE), this paper proposes the first multi-source collaborative estimation framework integrating high-resolution satellite imagery with ground-based sensor data. Methodologically, we employ computer vision techniques to extract multi-class vehicle density features from satellite images and construct a computation-graph-based heterogeneous data fusion model, enabling joint calibration and optimization of satellite-derived densities and conventional sensor measurements. Key contributions include: (i) the first incorporation of remote sensing imagery into dynamic OD estimation; and (ii) a lightweight, scalable graph-structured fusion paradigm. Experiments demonstrate substantial improvements in both overall network-wide OD estimation accuracy and robustness—particularly on detector-free links—while validating effectiveness and deployability on large-scale urban transportation networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

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 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 model that calibrates dynamic network states by jointly matching observed traffic 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.

Problem

• Estimating dynamic origin-destination demand using satellite imagery
• Overcoming data limitations with city-wide traffic information
• Improving estimation accuracy for links without local sensors

Innovation

• Leverages high-resolution satellite imagery for traffic data
• Computer vision pipeline for vehicle detection and mapping
• Computational graph-based DODE model for network calibration