Spatiotemporal Ego-Graph Domain Adaptation for Traffic Prediction With Data Missing

As an important research field in time series processing, traffic prediction has a profound impact on people’s daily lives and social development. Conventional traffic prediction relies on complete observation data. However, data missing is common in cities due to equipment failure, network interruption, etc., which poses a huge obstacle to traffic prediction. In this paper, we design a novel Spatiotemporal Ego-graph Domain Adaptation framework (SEDA) to predict traffic state in data missing scenarios. Based on the multi-dimensional topological information of local network (ego-graph), isomorphic ego-graphs are aligned across the missing data in target domain and the external data in source domain to obtain alternative data. Furthermore, a Dual-branch Cross reCoupling method (DCC) is proposed to reconstruct missing features according to the alternative data. Experimental results on real public datasets with 10%-40% missing show that SEDA averagely outperforms both the state-of-the-art knowledge transfer-based prediction baselines and the incomplete data prediction baselines by more than 0.45% and 0.86%. Ablation experiments and visualization analysis further demonstrate the effectiveness of SEDA components.