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.

本文提出了一种基于潜在空间模型的道路网络（LSM-RN）用于解决实时交通预测的问题，该模型能够学习路网快照的属性，并根据时间依赖性进行实时交通预测，通过与大量真实交通传感器数据的实验比较也展示了其优越性。