Meta-STMF: Meta-Learning Based Spatial Temporal Prediction Model Fusion Approach

Abstract

Traffic prediction is one of the core technology of intelligent transportation systems and is of great importance in traffic management. Among the existing methods, deep learning-based traffic prediction models are the mainstream methods and have achieved decent performance. However, when faced with traffic data with diverse traffic patterns, one model has to divide its attention to account for the prediction performance in different patterns, thus degrading the overall performance. To overcome the limitation, this paper proposes a meta-learning-based spatial-temporal deep learning model fusion approach, called Meta-STMF. The main idea is to assemble a group of sub-models so that each sub-model can focus on the patterns it specializes in. The method first trains each sub-model separately. Then, it extracts meta-knowledge from the input traffic data and utilizes a meta-learner to generate adaptive combination weights for each sub-model. Finally, the combination weights are used in the fusion of every sub-model prediction to obtain the final prediction. Extensive experiments on two large-scale real-world datasets show that our proposed approach consistently outperforms all competing baseline models with a large margin.