STG-Meta: Spatial-Temporal Graph Meta-Learning for Traffic Forecasting

Abstract

For current traffic datasets, data scarcity is common in many districts. This limits the performance of existing spatial-temporal models. Current works tackle this problem by transferring knowledge from other cities. They mainly focus on grid-like data, thus not sharing graph structure information across cities. However, for graph-structured data like highway traffic flow, the spatial dependency between nodes is much more obvious. Furthermore, existing research focuses solely on data inconsistency between cities, ignoring data inconsistency in different periods. Towards these, we propose STG-Meta, a meta-learning-based framework for graph-based traffic prediction tasks with only limited training samples. Specifically, STG-Meta adopts the cross-city-cross-period task construction method to reflect the variation in data across periods. STG-Meta includes the structure memory to store the embedding of the structure patterns. Additionally, the optimization-based meta-learning method is utilized to extract knowledge such as the memory and the initialization parameters of spatial-temporal graph (STG) networks, from other cities. Experiments on realworld datasets of two traffic data types demonstrate that our method outperforms state-of-the-art approaches for data-scarce cities.