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Accurate traffic state prediction is the foundation of transportation controland guidance. It is very challenging due to the complex spatiotemporaldependencies in traffic data. Existing works cannot perform well for multi-steptraffic prediction that involves long future time period. The spatiotemporalinformation dilution becomes serve when the time gap between input step andpredicted step is large, especially when traffic data is not sufficient ornoisy. To address this issue, we propose a multi-spatial graph convolutionbased Seq2Seq model. Our main novelties are three aspects: (1) We enrich thespatiotemporal information of model inputs by fusing multi-view features (time,location and traffic states) (2) We build multiple kinds of spatialcorrelations based on both prior knowledge and data-driven knowledge to improvemodel performance especially in insufficient or noisy data cases. (3) Aspatiotemporal attention mechanism based on reachability knowledge is novellydesigned to produce high-level features fed into decoder of Seq2Seq directly toease information dilution. Our model is evaluated on two real world trafficdatasets and achieves better performance than other competitors.

Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting