预测时变交通量的道路网络潜空间模型
Latent Space Model for Road Networks to Predict Time-Varying Traffic
摘要：
从高保真时空交通传感器数据集进行实时交通预测是智能交通系统和可持续发展的重要问题。然而，由于复杂的拓扑依赖性和与不断变化的道路条件相关的高动态性，这是具有挑战性的。在本文中，我们提出了一个潜在的道路网络空间模型（LSM-RN），以解决这些挑战。特别是，给定一系列的道路网络快照，我们学习的属性的顶点在潜在的空间，捕捉拓扑和时间属性。由于这些潜在属性是时间相关的，它们可以估计流量模式如何形成和演变。此外，我们提出了一个增量式的在线算法，顺序和自适应地学习的潜在属性的时间图的变化。我们的框架通过以下方式实现实时交通预测：1）利用实时传感器读数来调整/更新现有的潜在空间，2）在数据到达时进行训练，并使用给定的数据进行实时预测。通过对大量真实世界的交通传感器数据进行广泛的实验，我们证明了我们的框架在大型道路网络上的实时交通预测方面优于竞争对手以及基于基线图的LSM的实用优势。
英文摘要：
Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learn the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly with given data. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks over competitors as well as a baseline graph-based LSM.
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