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 dynamism associated with changing road conditions. In this paper, we pro-pose a Latent Space Model for Road Networks (LSM-RN) to ad-dress these challenges holistically. 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 learns the latent attributes from the temporal graph changes. Our frame-work 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. By con-ducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the superiority of our frame-work for real-time traffic prediction on large road networks over competitors as well as baseline graph-based LSM’s.

Filed under: Mining Rich Data Types