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Urban traffic speed prediction aims to estimate the future traffic speed forimproving the urban transportation services. Enormous efforts have been made onexploiting spatial correlations and temporal dependencies of traffic speedevolving patterns by leveraging explicit spatial relations (geographicalproximity) through pre-defined geographical structures ({\it e.g.}, regiongrids or road networks). While achieving promising results, current trafficspeed prediction methods still suffer from ignoring implicit spatialcorrelations (interactions), which cannot be captured by grid/graphconvolutions. To tackle the challenge, we propose a generic model for enablingthe current traffic speed prediction methods to preserve implicit spatialcorrelations. Specifically, we first develop a Dual-Transformer architecture,including a Spatial Transformer and a Temporal Transformer. The SpatialTransformer automatically learns the implicit spatial correlations across theroad segments beyond the boundary of geographical structures, while theTemporal Transformer aims to capture the dynamic changing patterns of theimplicit spatial correlations. Then, to further integrate both explicit andimplicit spatial correlations, we propose a distillation-style learningframework, in which the existing traffic speed prediction methods areconsidered as the teacher model, and the proposed Dual-Transformerarchitectures are considered as the student model. The extensive experimentsover three real-world datasets indicate significant improvements of ourproposed framework over the existing methods.

Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations