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.

翻译：本研究提出了一种新颖的集成框架，用于多类别中观网络模型中的动态起讫点需求估计，该框架将高分辨率卫星影像与来自本地传感器的传统交通数据相结合。与稀疏的本地检测器不同，卫星影像能够提供覆盖整个城市、具有一致性的道路与交通信息，包括停放与行驶中的车辆，从而克服了数据可用性的限制。为了从影像数据中提取信息，我们设计了一个计算机视觉流程，用于特定类别车辆的检测与地图匹配，从而按车辆类别生成路段级别的交通密度观测值。基于这些信息，我们构建了一个基于计算图的动态起讫点需求估计框架，该框架通过联合匹配来自本地传感器的观测交通流量/速度与源自卫星影像的密度测量值，来校准动态网络状态。为了评估所提框架的准确性与鲁棒性，我们使用合成数据与真实世界数据进行了一系列数值实验。结果表明，利用卫星衍生的密度数据补充传统数据，能显著提升估计性能，特别是对于没有本地传感器的路段。真实世界实验也展示了该框架在大规模网络上实际部署的潜力。敏感性分析进一步评估了与卫星影像数据相关的数据质量的影响。