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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 propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges holistically. In particular, given a series of road network snapshots, we learn the attributesdoi:10.1145/2939672.2939860 dblp:conf/kdd/DengSDZYL16 fatcat:xywooi5wnbhavkrfj4csz7uyda