Abstract Traffic volume is a critical piece of information in many applications, such as transportation long-range planning and traffic operation analysis. Effectively capturing traffic volumes on a network scale is beneficial to Transportation Systems Management & Operations (TSM&O). Yet it is impractical to install sensors to cover a large road network. To address this issue, spatial prediction techniques are widely performed to estimate traffic volumes at sites without sensors. In retrospect, most relevant studies resort to machine learning methods and treat each prediction location independently during the training process, ignoring the potential spatial dependency among them. This paper presents an innovative spatial prediction method of hourly traffic volume on a network scale. To achieve this, we applied a state-of-the-art tree ensemble model - extreme gradient boosting tree (XGBoost) - to handle the large-scale features and hourly traffic volume samples, due to the model's powerful scalability. Moreover, spatial dependency among road segments is taken into account in the proposed model using graph theory. Specifically, we created a traffic network graph leveraging probe trajectory data, and implemented a graph-based approach - breadth first search (BFS) - to search neighboring sites in this graph for computing spatial dependency. The proposed spatial dependency feature is subsequently incorporated as a new feature fed into XGBoost. The proposed model is tested on the road network in the state of Utah. Numerical results not only indicate high computational efficiency of the proposed model, but also demonstrate significant improvement in prediction accuracy of hourly traffic volume comparing with the benchmarked models.

中文翻译：

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使用数据驱动的机器学习和图论推断每小时的交通量

摘要 交通量是交通远景规划、交通运行分析等诸多应用中的重要信息。有效地捕获网络规模的交通量有利于运输系统管理和运营 (TSM&O)。然而，安装传感器来覆盖大型道路网络是不切实际的。为了解决这个问题，广泛使用空间预测技术来估计没有传感器的站点的交通量。回想起来，大多数相关研究都采用机器学习方法，并在训练过程中独立处理每个预测位置，而忽略了它们之间潜在的空间依赖性。本文提出了一种创新的网络尺度每小时交通量空间预测方法。为了达成这个，由于该模型具有强大的可扩展性，我们应用了最先进的树集成模型——极端梯度提升树（XGBoost）——来处理大规模特征和每小时流量样本。此外，在使用图论提出的模型中考虑了路段之间的空间依赖性。具体来说，我们利用探测轨迹数据创建了一个交通网络图，并实施了一种基于图的方法 - 广度优先搜索 (BFS) - 在该图中搜索相邻站点以计算空间依赖性。提出的空间依赖特征随后作为一个新特征被纳入 XGBoost。所提出的模型在犹他州的道路网络上进行了测试。数值结果不仅表明所提出模型的计算效率高，