Metropolitan traffic networks are becoming increasingly complex due to the growing population and diverse range of travel modes. However, the limited installation of continuous count stations leads to partially observable networks, posing a significant challenge for effective highway planning and design practices at various scales. Travel demand models have been developed and calibrated using sparse traffic counts at the metropolitan level. Nevertheless, these models are cumbersome to recalibrate and rerun whenever network changes occur. To overcome this challenge, we propose a flexible learning-based approach that extracts embedded knowledge from large-scale activity-based travel demand models to estimate Annual Average Daily Traffic (AADT). The approach offers two primary advantages: (1) directly learning network flow patterns based on segment attributes and network topology that can be transferred across regions, and (2) enabling efficient and reliable AADT estimation for projects of various scales. Our study explores a wide range of machine learning techniques, including novel graph neural networks that explicitly account for network topology, as well as modern and traditional regression and regression kriging models, which either disregard or implicitly consider network topology. We conducted extensive experiments using the loaded network data from the activity-based travel demand model for the Atlanta metropolitan area. Our findings underscore the importance of network topology in AADT estimation, with the diffusion graph convolutional network model demonstrating the best performance in both transductive and inductive settings. Additionally, modern tree ensemble models such as random forest regressor and CatBoost, despite their ignorance of network topology, show the second-best inductive performance with relatively lightweight structures.

由于人口的增长和出行方式的多样化，大城市的交通网络变得越来越复杂。然而，连续计数站的安装有限导致网络部分可观测，这对不同规模的有效公路规划和设计实践提出了重大挑战。出行需求模型是利用大都市层面的稀疏交通流量来开发和校准的。然而，每当网络发生变化时，这些模型重新校准和重新运行都很麻烦。为了克服这一挑战，我们提出了一种灵活的基于学习的方法，该方法从大规模基于活动的出行需求模型中提取嵌入式知识来估计年平均每日交通量（AADT）。该方法具有两个主要优点：(1) 基于可跨区域传输的分段属性和网络拓扑直接学习网络流模式，(2) 为各种规模的项目提供高效、可靠的 AADT 估计。我们的研究探索了广泛的机器学习技术，包括显式考虑网络拓扑的新型图神经网络，以及现代和传统的回归和回归克里金模型，这些模型要么忽略或隐式考虑网络拓扑。我们使用从亚特兰大大都市区基于活动的出行需求模型加载的网络数据进行了广泛的实验。我们的研究结果强调了网络拓扑在 AADT 估计中的重要性，扩散图卷积网络模型在传导和归纳设置中都表现出了最佳性能。 此外，现代树集成模型（例如随机森林回归器和 CatBoost）尽管不了解网络拓扑，但在相对轻量级的结构中表现出第二好的归纳性能。