A transfer approach with attention reptile method and long-term generation mechanism for few-shot traffic prediction

Spatial–temporal prediction is a fundamental problem for intelligent transportation system (ITS), which is important for traffic management such as vehicle controls and travel plans. Deep learning has achieved success in spatial–temporal prediction with adequate data. However, many cities still suffer from data scarcity due to lack of essential infrastructure and services for data collection. Therefore, spatial–temporal prediction of cities with scarce data can be regarded as a few-shot learning problem. In this paper, we propose a novel transfer learning method to tackle spatial–temporal prediction tasks with only a small collection of data. Our proposed model aims to transfer the knowledge from multiple source cities to the target city by considering the different spatial–temporal distribution similarities between cities. Specifically, our model is designed as a spatial–temporal network based on a first-order meta-learning algorithm Reptile with an attention mechanism. Different from meta-learning algorithms that aim to learn a well-generalized initialization which can be adapted to any new task, our model achieves better performance on the specific target city by considering the different distribution similarities between multiple source cities and the target city. In addition, as it is difficult to learn the long-term spatial–temporal dependency with limited data in the target city, we propose a generation mechanism to learn and transfer long-term temporal features from source cities which have abundant long-period data to the target city. In the experiments, we compare our model with other state-of-the-art methods in real-world traffic prediction task. The experiments demonstrate the effectiveness of the proposed model.