  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.
